MAKING COLLABORATIVE DATA PHYSICALISATIONS

Leo Alireza Rezayan Master of Creative Industries (IVD)

A thesis submitted in fulfilment of the requirements

for the degree of Doctor of Philosophy

2019

Urban Informatics Research Group, QUT Design Lab School of Design, Creative Industries Faculty Queensland University of Technology

Much of the work of this PhD was conducted in and around Brisbane, Australia.

In keeping with the spirit of Reconciliation, I acknowledge the Traditional Owners of the lands where QUT now stands – and recognise that these have always been places of teaching and learning.

I wish to pay respect to their Elders – past, present and emerging – and acknowledge the important role Aboriginal and Torres Strait Islander people continue to play within the QUT community.

Making Collaborative Data Physicalisations i

ii Making Collaborative Data Physicalisations Keywords

Physicalisation, physical visualisation, tangible data presentation, physical data pattern, physical data experience, tangible interaction, democratising visualisation, constructive visualisation, digital fabrication, tangible and physical systems, data and information design

Physicalization, physical visualization, democratising visualization, constructive visualization

Making Collaborative Data Physicalisations iii

iv Making Collaborative Data Physicalisations Abstract

Data visualisations appear to be ubiquitous in contemporary life, from train and bus maps to census statistics to graphs on utility bills. The most common sorts of visualisations, whether three-dimensional or two-dimensional, are presented in a flat space on paper or a screen. This project aims to design, create and test a physical data presentation physicalisation from scratch to escape what Edward Tufte calls the ‘flatland’; he uses the term flatland to describe the limited two-dimensional environments of paper and screen. This approach offers a deeper engagement with physicalisation and data-mapping activities in the context of use. This project first reviews the field of tangible interaction and collaboration to identify a series of concepts to support the design of collaborative data physicalisation. Next, this research undertakes a research through design and reflective approach to design to create a new, collaborative data physicalisation system. It then uses observations and focus-groups to evaluate the design’s utility and explore how people employ physicalisation as part of their collaborative sense-making and meaning-making. This project contributes knowledge for the design and creation of collaborative physicalisation systems that can be used and reused to present a variety of data sets, and also highlights the processes of sense-making and meaning-making that people engage in when using these systems. The results of this thesis provide new knowledge for how to move data presentations beyond the visual to facilitate multi-sensory interaction and encourage collaborative engagement with data.

Making Collaborative Data Physicalisations v

vi Making Collaborative Data Physicalisations Table of Contents

Keywords...... iii Abstract ...... v Table of Contents ...... vii List of Figures ...... xiii List of Tables ...... xvii Statement of Original Authorship ...... xix Acknowledgements ...... xxi Chapter 1: Introduction ...... 1 1.1 Background ...... 1 1.2 Context ...... 2 1.3 Purposes ...... 3 1.4 Significance and Scope...... 4 1.5 Thesis Structure ...... 6 Chapter 2: Literature Review...... 9 Chapter overview ...... 9 2.1 Introduction ...... 9 Digital Fabrication and Data Physicalisation ...... 10 2.2 Surveying the Field of Data Physicalisation ...... 11 Historical Data Physicalisations ...... 12 Data Sculptures ...... 13 Dynamic Data Sculptures ...... 15 Data Physicalisations Based on Personal Data-Sets ...... 16 Data Physicalisations Using Concrete Scale and Metaphors ...... 18 Interactive Data Physicalisations ...... 20 Data Physicalisations and Collaboration ...... 21 2.3 Concepts from Data and Information Visualisations ...... 22 Democratisation ...... 23 Constructive Visualisation ...... 23 Minimal Set of Requirements for Information Visualisation...... 24 2.4 Theoretical Perspectives from HCI ...... 26 Embodied Interaction and Affordances ...... 26 Boundary Objects ...... 28 Token and Constraint ...... 29 Common Examples of Mappings ...... 32 Some of the Advantages of the Token and Constraint Approach ...... 33 Concepts from HCI ...... 34 Some of the Different Kinds of Tokens ...... 35 Perspectives on Collaboration from Tangible Interaction and Interface Design ...... 38 Interpersonal Interaction ...... 43 Fluid Transition ...... 43 Shared Access ...... 43

Making Collaborative Data Physicalisations vii Access Point ...... 44 Flexible User Arrangement ...... 44 Non-Fragmented Visibility ...... 45 Simultaneous Actions...... 45 Direct Manipulation ...... 45 Simple Interaction ...... 46 Externalisation ...... 46 The Gap in the Literature for Data Physicalisation ...... 47 2.5 Chapter Summary ...... 48 Chapter 3: Design Research ...... 51 3.1 Methodology and methods ...... 51 Design Research; Design and Research ...... 53 Design Research and Research Through Design Approach ...... 56 Design as Part of the Research Process ...... 56 Tangible Interaction ...... 59 3.2 Participants ...... 60 3.3 Instruments; Research Methods ...... 60 Observation ...... 61 Design Evaluation; Focus Group and Group Interview ...... 66 3.4 Procedure and Methods ...... 67 Data-Collection Methods ...... 67 Audio-Visual ...... 67 Semi-Structured Interview ...... 68 Field Notes ...... 68 3.5 Analysis ...... 69 Thematic Analysis ...... 69 Semantic or Latent ...... 70 Conducting Thematic Analysis ...... 71 1. Becoming Familiar with collected data: ...... 72 2. Generating initial codes: ...... 72 3. Searching for themes: ...... 73 4. Reviewing themes: ...... 73 5. Defining and naming themes: ...... 73 6. Producing the report: ...... 73 3.6 Summary of Ethical Considerations...... 74 Design Evaluation and Group Interview ...... 76 Chapter 4: Design Development...... 79 4.1 Introduction ...... 79 4.2 Design of the System ...... 80 Form Making ...... 80 Testing the Initial System to Represent a Data Set ...... 85 Physical Tokens to Represent Rain-Data in Brisbane ...... 87 The Lesson from The Abacus ...... 87 Creative Exploration with Peers as a Part of the Design Development ...... 90 Design Insights ...... 91 Shared Physical Tokens for Shared Interaction ...... 91 4.3 Context of Use: ...... 93 Getting Familiar with Energy Consumption at the Household Level ...... 94 Containers, Tokens, and Tools...... 97 Mapping on the Third dimension, the Z-Axis ...... 98

viii Making Collaborative Data Physicalisations Design Insights ...... 98 4.4 Designing the Final System and Final Activity Sessions ...... 99 Design of the Tokens ...... 99 Design of the Constraints ...... 100 Testing with my Peers as a Part of Design Development ...... 102 Design Insights ...... 102 Design of the Board and Finalising Physical Constraints ...... 104 Design of the Tip-Cards, Energy Consuming Categories and Phicons ...... 107 Energy Consuming Categories ...... 107 Tip-Cards ...... 108 Phicons ...... 109 The Final System and the Design Evaluation Activity Sessions ...... 110 Some of my Thinking at the Time of Designing the Activity ...... 112 4.5 Contribution to knowledge ...... 113 4.6 Chapter Summary ...... 115 Chapter 5: Results and Analysis...... 117 5.1 Introduction ...... 117 5.2 Overview of the Design evaluation Activity sessions ...... 118 Evaluating the Success of the Sessions ...... 120 Forming the Meta-Qualities ...... 123 Example of Interactions ...... 123 5.3 Observation, Analysis and Reflection on Meta-Qualities ...... 125 In Touch with Data; Direct Interaction with Data ...... 126 Direct Interaction; To Demonstrate ...... 126 Direct Interaction; To Remember ...... 128 Direct Interaction; To Highlight...... 129 Direct Interaction; To Review ...... 131 Direct Interaction; To Ask a Question ...... 132 Direct Interaction; To Explain ...... 132 Direct Interaction; To Relate and Reasoning ...... 133 Direct Interaction; To Assist ...... 134 Real-World Perspective and Access ...... 134 Real-World Perspective and Access; To Support Collaboration ...... 135 Real-World Perspective and Access; To Remind ...... 136 Real-World Perspective and Access; To Support Fluid Transitions ...... 137 Real-World Perspective and Access; To Support Paying Attention ...... 138 Real-World Perspective and Access; To Support Multi-Tasking and Work Beyond the Interface ...... 140 Real-World Perspective and Access; To Support Simple Interaction ...... 141 Real-World Perspective and Access; To Support Fluid Transition ...... 142 Playful Physical Interaction with Tokens ...... 144 Playful Physical Interaction with Tokens; When Listening ...... 144 Playful Physical Interaction with Tokens; When Listening, Answering and Demonstrating ...... 145 Playful Physical Interaction with Tokens; When Thinking Out Loud and Mapping ...... 147 Playful Physical Interaction with Tokens; When Thinking Out Loud and Demonstrating ...... 148 Playful Physical Interaction with Tokens; When Thinking ...... 149 Physical System and Collaboration ...... 151 Physical System and Collaboration; Working in Parallel ...... 151 Physical System and Collaboration; Working in Parallel and Under Assumed Roles ...... 153

Making Collaborative Data Physicalisations ix Physical System and Collaboration; Working in Parallel, Undo and Continue .. 154 Physical System and Collaboration; Working Sequentially and In Parallel ...... 155 Physical System and Collaboration; Working Sequentially ...... 156 Physical System and Collaboration; Working Independently ...... 157 Physical System and Communication ...... 158 Physical System and Communication; Direct Interaction ...... 159 Physical System and Communication; Externalisation ...... 160 Physical System to Support Externalisation ...... 162 Physical System to Support Externalisation; To Ask a Question ...... 163 Physical System to Support Externalisation; To Communicate ...... 164 Physical System to Support Externalisation; To Represent Data ...... 165 Physical System to Support Externalisation; To Explain Mapping ...... 166 Physical System to Support Externalisation; To Think Out Loud and Communicate ...... 167 Physical System to Support Externalisation; Think Out Loud with the System . 169 Physicalisation and Enjoyment ...... 171 Physicalisation and Enjoyment; Experiencing Magnetic Force ...... 171 Physicalisation and Enjoyment; Physical Mapping and Working Together ...... 172 Physicalisation and Enjoyment; The ‘other’ Category ...... 173 Physicalisation and Enjoyment; Overall Feedback ...... 176 5.4 Summary of Meta-Qualities ...... 176 In Touch with Data, Direct Interaction with Data ...... 178 Real-World Perspective and Access ...... 178 Playful Physical Interaction with Tokens ...... 178 Physical System and Collaboration ...... 179 Physical System and Communication ...... 179 Physical System to Support Externalisation ...... 179 Physicalisation and Enjoyment ...... 180 Physicalisation and Sense-Making and Meaning-Making process ...... 180 5.5 Chapter Summary ...... 180 Chapter 6: Discussion and Conclusion ...... 183 6.1 Discussion ...... 183 Detailed Discussion of Research Questions and Answers...... 184 RQ1 What concepts from tangible interaction and information design literature can be applied to support collaborative physicalisation? ...... 184 RQ2 How can a collaborative physicalisation system be designed and made? .. 187 Design a System ...... 189 Constructive Visualisation ...... 189 System as a Toolkit Set ...... 189 Decide what the system can offer ...... 189 Design the Interactions ...... 189 Design-Make-Test-Reflect ...... 190 Design Practice Informed by Theory ...... 190 Get to Know your Data-set ...... 191 Finalising the System ...... 191 RQ3 How do people employ data physicalisation as part of their collaborative sense-making and meaning-making? ...... 192 6.2 Contribution to Knowledge ...... 198 The Uniqueness of Design for the Physicalisation...... 198 Designing a New Kind of Visualisation ...... 199 Data for Makers ...... 200 System vs Artefact ...... 201 Democratising Visualisation Through Constructive Physicalisation ...... 201 6.3 Limitations and Future Work ...... 203

x Making Collaborative Data Physicalisations Physicalisation in and for VR and AR ...... 204 6.4 Conclusion ...... 205 References ...... 207 Appendices ...... 217 6.5 Appendix 1, Energy-Efficiency Tips ...... 217 Heating and Cooling ...... 217 In the Kitchen ...... 218 Laundry ...... 219 Lighting ...... 220 Hot Water ...... 220 Standby Power ...... 221 Some Other Online Sources ...... 221 6.6 Appendix 2, Data Mapped to the Physicalisation System ...... 222 Design Evaluation Session 1 ...... 222 Design Evaluation Session 2 ...... 223 Design Evaluation Session 3 ...... 224 Design Evaluation Session 4 ...... 225 Design Evaluation Session 5 ...... 226 All Five Design Evaluation Sessions ...... 227 6.7 Appendix 3, Questions Asked from Participants ...... 228 List of Questions Asked from Participants During Each Design Evaluation Session ...... 228

Making Collaborative Data Physicalisations xi

xii Making Collaborative Data Physicalisations List of Figures

Figure 1.a. Plain tokens, b. Complex tokens...... 12 Figure 2. 1930 Detroit Edison Company Physical Visualisation ...... 12 Figure 3.a. Keyboard Frequency Sculpture, b. Measuring Cup, c. Sweat-Atoms ...... 13 Figure 4.a. Centograph, b. Emoto, c. eCLOUD ...... 15 Figure 5.a. Data Necklace, b. Meshu, c. DNA Jewellery ...... 16 Figure 6.a. Bigger DNA JEWELLERY 3D printed in plastic, b. Smaller DNA JEWELLERY 3D printed in silver; It is much easier to read the data from the bigger artefacts 3D printed in plastic (Figure 6.a) compared to the much smaller artefact 3D printed in silver (Figure 6.b)...... 17 Figure 7.a. How Much Sugar Do You Consume: Amount of sugar in different size Coke, b. Can We Keep Up? Sponges map, c. Of All the People in the World ...... 18 Figure 8.a. From over here, b. Inverted collaborative bar charts, c. MakerVis ...... 20 Figure 9.a. General Motors’ 3D LEGO Visualisations, b. Building visualisations with tokens ...... 21 Figure 10.a. Parallel Sets show data about the people on the Titanic which is readable and recognisable as a visualisation b. MilkDrop is Music visualisation which is data driven, but not readable ...... 24 Figure 11. Collaborative data physicalisation at the intersection of Tangible Interaction, Data Physicalisation and Collaborative Tangible Interface Design ...... 39 Figure 12. Research questions and research through design approach ...... 52 Figure 13. A cycle of iteration in this research ...... 58 Figure 14. Research methods at different phases of the study ...... 61 Figure 15. Researcher position in the observation ...... 63 Figure 16.a. Flat hexagon 6x6mm and their spatial arrangement, b. Example of tessellation, c. Actual picture of the unit ...... 81 Figure 17. 6x6mm hexagons with positive and negative surfaces and their spatial arrangement, b. Example of tessellation, c. Actual picture of the unit ...... 82 Figure 18. 10x10mm hexagons with holes and their spatial arrangement, b. Example of positive/negative caps, with positive and negative surfaces, c. Actual picture of the unit ...... 83 Figure 19. 10x10mm pair-hexagons with a flat cap and no cap and their spatial arrangement, b. 10x10mm pair-hexagons unit with a flat cap and no cap, c. Actual picture of the unit ...... 84 Figure 20. 10x10mm hexagons with one positive extension and five negative connectors and their spatial arrangement, b. Example of tessellation, c. Actual picture of the unit ...... 85 Figure 21.a. Lego size hexagonal unit with positive extension and negative connector, b. From a top perspective and from left to right units represented: 1. Void or Empty, 2. Void-Uppermost, 3. Mass or Flat, 4. Flat-Uppermost. c. Actual picture of the unit ...... 88

Making Collaborative Data Physicalisations xiii Figure 22. Data mapped based on Table 10; ...... 90 Figure 23. Week 1 mapping five different energy consuming activities at my household...... 95 Figure 24. Illustrated version of Lego-board on (Figure 8) ...... 96 Figure 25. Week 2 mapping six different energy consuming activities at my household with vertical columns for a personal goal ...... 97 Figure 26. Seven columns to represent seven days of a week; ...... 98 Figure 27. Magnetic Cubes in seven different colours...... 100 Figure 28. Weekly Chart, days are divided into four time zones from early morning to night ...... 101 Figure 29. Weekly Chart mounted on a whiteboard with stand ...... 101 Figure 30. Chart with individual columns for two colleagues and house for the last 48hr (separate column for each day and divided into four time-zones) ...... 103 Figure 31. a. 9x9 chart, seven middle columns represent days of the week, and seven middle rows represent different energy consuming activity...... 105 Figure 32. The final board made of MDF and Magnets ...... 106 Figure 33. Top: coloured pattern inspired by the tokens for each category...... 109 Figure 34. Phicons; Top: top surface with engraved text, Bottom: bottom surface with an embedded magnet ...... 109 Figure 35. Activity in progress with household members ...... 111 Figure 36. Complex example ...... 124 Figure 37. User arrangement during each session. From left to right: a. Session 1, b. Session 2, c. Session 3, d. Session 4, and e. Session 5 ...... 125 Figure 38. Session 2, Direct interaction to demonstrate ...... 127 Figure 39. Session 4, Direct interaction to demonstrate ...... 127 Figure 40. Session 4, Direct interaction to demonstrate ...... 128 Figure 41. Session 2, Direct interaction to remember ...... 129 Figure 42. Session 4, Direct interaction to highlight ...... 129 Figure 43. Session 2, Direct interaction to highlight ...... 130 Figure 44. Session 5, Direct interaction to highlight ...... 130 Figure 45. Session 4, Direct interaction to review ...... 131 Figure 46. Session 1, Direct interaction to ask a question...... 132 Figure 47. Session 3, Direct interaction to explain ...... 132 Figure 48. Session 2, Direct interaction to relate and reasoning ...... 133 Figure 49. Session 4, Direct interaction to assist ...... 134 Figure 50. Session 3, Real-World perspective and access to support collaboration by providing ...... 136 Figure 51. Session 3, Real-World perspective and access to support collaboration by complete mapping ...... 136 Figure 52. Session 2, Real-World perspective and access, to remind ...... 137

xiv Making Collaborative Data Physicalisations Figure 53. Session 2, Real-World perspective and access to support fluid transitions ...... 137 Figure 54. Session 3, Participants working together by mapping simultaneously ...... 138 Figure 55. Session 3, Real-World perspective and access to support paying attention...... 139 Figure 56. Session 1, Real-World perspective and access to support multi-tasking and work beyond the interface ...... 140 Figure 57. Session 2, Real-World perspective and access to support work beyond the interface ...... 141 Figure 58. Session 1, Real-World perspective and access to support simple interaction ..... 142 Figure 59. Session 2, Real-World perspective and access to support the fluid transition .... 142 Figure 60. Session 1, Playful physical interaction with tokens when listening ...... 145 Figure 61. Session 3, Playful physical interaction with tokens when listening ...... 145 Figure 62. Session 4, Playful physical interaction with tokens when listening, answering and demonstrating ...... 146 Figure 63. Session 1, Playful physical interaction with tokens when thinking out loud and mapping ...... 147 Figure 64. Session 4, Playful physical interaction with tokens when thinking out loud and mapping ...... 148 Figure 65. Session 5, Playful physical interaction with tokens when thinking out loud and demonstrating ...... 149 Figure 66. Session 1, Playful physical interaction with tokens when thinking...... 150 Figure 67. Session 2, Physical system and collaboration; working in parallel ...... 151 Figure 68. Session 4, Physical system and collaboration; working in parallel ...... 152 Figure 69. Session 5, Physical system and collaboration; working in parallel ...... 153 Figure 70. Session 2, Physical system and collaboration; working in parallel and under assumed roles ...... 154 Figure 71. Session 2, Physical system and collaboration; working in parallel, undo and continue ...... 155 Figure 72. Session 1, Physical system and collaboration; working sequentially and in parallel ...... 156 Figure 73. Session 2, Physical system and collaboration; working sequentially ...... 156 Figure 74. Session 4, Physical system and collaboration; working independently ...... 158 Figure 75. Session 2, Physical system and communication; direct interaction ...... 159 Figure 76. Session 2, Physical system and communication; direct interaction ...... 160 Figure 77. Session 3, Physical system and communication; externalisation ...... 161 Figure 78. Session 3, Physical system and communication; externalisation ...... 161 Figure 79. Session 1, Physical system to support externalisation; to ask a question ...... 163 Figure 80. Session 4, Physical system to support externalisation; to ask a question ...... 164 Figure 81. Session 4, Physical system to support externalisation; to communicate ...... 165 Figure 82. Session 1, Physical system to support externalisation; to represent data ...... 166 Figure 83. Session 4, Physical system to support externalisation; to explain the mapping ...... 166

Making Collaborative Data Physicalisations xv Figure 84. Session 2, Physical system to support externalisation; to explain the mapping ...... 167 Figure 85. Session 4, Physical system to support externalisation; to think out loud and communicate ...... 168 Figure 86. Session 4, Physical system to support externalisation; to think out loud and communicate ...... 168 Figure 87. Session 1, Physical system to support externalisation; to think out loud and communicate ...... 169 Figure 88. Session 4, Physical system to support externalisation; think out loud with the system ...... 170 Figure 89. Session 1, Physicalisation and enjoyment; experiencing a magnetic force ...... 171 Figure 90. Session 2, Physicalisation and enjoyment; experiencing a magnetic force ...... 172 Figure 91. Session 3, Physicalisation and enjoyment; physical mapping and working together ...... 173 Figure 92. Session 2, Physicalisation and enjoyment; the ‘other’ category ...... 174 Figure 93. Session 1, Physicalisation and enjoyment; the ‘other’ category ...... 174 Figure 94. Session 2, Physicalisation and enjoyment; the ‘other’ category ...... 175 Figure 95. Session 2, Physicalisation and enjoyment; the ‘other’ category ...... 175 Figure 96. Session 5, Physicalisation and enjoyment; overall feedback ...... 176

xvi Making Collaborative Data Physicalisations List of Tables

Table 1. Research Questions, Methods and Outcomes ...... 4 Table 2. The Minimal set of requirements for any visualisation by Kosara ...... 25 Table 3. The terminology used in this project; ...... 36 Table 4. Factors and Effects summarised by Ullmer for Cooperative Use of TUIs; ...... 37 Table 5. Suggested design guidelines to support collaboration and communication; ...... 40 Table 6. Themes and Concepts on the Tangible Interaction Framework; ...... 41 Table 7. Identified characteristics for designing a collaborative physicalisation ...... 42 Table 8. Summary of Archer’s, Frayling’s and Candy’s classifications ...... 55 Table 9. Phases of thematic analysis by Braun & Clarke ...... 72 Table 10. Daily Rainfall (millimetres) BRISBANE Station 2014 ...... 86 Table 11. Factors and Effects summarised by Ullmer for Cooperative Use of TUIs;...... 92 Table 12. Assigned colours for each energy-consuming-activity category ...... 108 Table 13. Connections between meta-qualities and identified characteristics;...... 118 Table 14. Research Questions and Answers ...... 184

Making Collaborative Data Physicalisations xvii

xviii Making Collaborative Data Physicalisations Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: May 2019

Making Collaborative Data Physicalisations xix

xx Making Collaborative Data Physicalisations Acknowledgements

This research study was supported by an Australian Postgraduate Award (APA), without this funding, I would not have been able to take up this opportunity.

Over the years, this work has evolved into where it stands today. Through many iterations of re-evaluation and transition, I have had the support and encouragement from many people around me. At the top of my list, I would like to thank my supervisors whose ongoing support and confidence in me, is something I will never forget.

Thank you, Dr Jared Donovan, my loyal and inspiring supervisor, for letting me start and go on. For dedicating so much time and for your honest and constructive feedback from the very beginning all the way to the end. And thank you Dr Jen Seevinck, your conceptual input and critical insights helped to polish the work and push into a higher level. Thank you both for your tremendous support and valuable feedback. Also special thanks to Prof. Marcus Foth for knowledge, wisdom, and believing in me.

Many thanks to the Creative Industries Research Centre, and HDR student office, especially Helena Papageorgiou for being available and responsive all the time and for all of your support. Many thanks to design lab fabrication studio at J block, in particular, Ian Ashworth and Simon Belton as well as Melissa Johnston, at the Science and Engineering Faculty, Technical Services office who supported me through the making and prototyping of the artefacts.

Many thanks to all the senior colleagues and role models that provided me with constructive feedback and gave me a chance to be part of their research and teaching team. To Dr Jaz Choi, Dr Mirko Guaralda, Assoc. Prof. Markus Rittenbruch, Dr Richard Medland, Dr Ronald Schroeter, Dr Glenda Caldwell, Mr Andrew Scott as well as Assoc. Prof. Tomasz Bednarz, and Dr Andrew George at CSIRO.

Many thanks to QUT Design Lab in particular, Urban Informatic Research Group, Ride Share and Shine my Writing friends and colleagues for your intellectual, emotional, social and practical support, for sharing your knowledge with me. To Dr. Levi Swann, Dr. Peter Lyle, Dr. Daniel Filonik, Dr. Leonarda Parra, Dr. Tiago Camacho, Dr. Müge Belek, Anna Sevensdotter, Irina Anastasia, Younghui Kim, Clare Villalba, Waldemar Jenek, Andrew Bates, Yu Kao, Ben Carden, Kamal Wasala, Alan Burden, and everyone else. Very Special thanks to Dr. Heather McKinnon and Carlos Estrada-Grajales for helping me with recruiting participants. Looking forward to working and collaborating with you all in the close future.

I would also like to acknowledge all households’ members who volunteered their time, energy, and creativity to be involved in this study – Thank you for your valuable time, insight and feedback. I would also like to thank everyone who inspired and supported me throughout my thesis. I could not have gotten through this without all you.

Most importantly, I want to thank my family for having faith in me, support and encouragement. To my father, Khosrow, and my mother, Marzi, you continued to believe in me and encouraged me to believe in myself. I cannot thank you enough for everything. Finally and above all, to my dearest wife, Bahar and our beloved son, Ryan. You are both so kind, patient, and devoted, and your unwavering love, laughter, and support have made this all possible. Love you so much.

Making Collaborative Data Physicalisations xxi

Chapter 1: Introduction

This chapter outlines the background (section 1.1), and context (section 1.2) of the research. Section 1.3, highlights the purposes and section 0, describes the significance and the scope of this research. Finally, section 1.5 includes an outline of this thesis structure.

1.1 BACKGROUND

Considering the latest and cutting-edge technologies for processing and computing; we are producing digital information every hour, minute and second in contemporary society; and much faster than we could process or make sense of this data1. Within this vast ocean of data lies a great deal of unexplored, precious information. We must find methods to experience, present, make sense, relate and communicate this information collaboratively and purposefully.

Since the formation of information visualisation as a field, there has been extensive research into various aspects of visualisations—however, surprisingly most of this research has been limited to two-dimensional (2D) environments of paper or digital screens. Edward Tufte writes of this as the “endless flatland of paper and video screen” and argues that “Escaping this flatland is the essential task of envisioning information” (Tufte, 1991, p. 12). Tufte uses the term flatland to describe the limited two-dimensional (2D) environments of paper and screen. Over the last few years, the will to visualise beyond the 2D space has become more apparent, and there have been more attempts to address this concern. I have provided some examples of such attempts in the Literature Review chapter under Data Sculptures section. However, most approaches still only consider the third dimension as an extension of existing 2D visualisations. The tangible, tactile and physical features often seem like an adoption from a foreign environment of the existing design within the 2D world. Sometimes these projects are the direct conversion of existing visualisations from a 2D to the 3D environment, for

1 We are creating “ 2.5 quintillion bytes of data each day at our current pace” (‘How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read’, 2018).

Chapter 1: Introduction 1

example, an extruded 3D graph manufactured in the physical environment based on a 2D graph on a screen. Although this conversion offers tangible and physical interaction with a visualisation, more is possible. One explanation for this shortcoming is the limited connection between tangible interaction and physical design with data and information visualisation.

Information visualisation in general and in physical form, in particular, has a longer history than writing (Schmandt-Besserat, 2009). Some of the prehistoric examples of such physical data and information visualisation Physicalisation goes back to more than 5000 years ago (e.g. Clay Tokens in the western part of Asia) (Schmandt-Besserat, 1996). The historical evidence indicates that early approaches towards data and information visualisations were purely physical and tangible (Yvonne Jansen, 2014). Conventional ways of thinking in contemporary society support the logic of visualisations changing through time and technology, from ancient clay tokens to painting and drawing on caves to paper and screen and, nowadays, on digital platforms. However, regardless of now being capable of producing and reproducing visualisations faster than before on the sharpest and highest resolution screens, we are not only missing the spatial aspects of our visualisations and becoming trapped within the 2D space but are also missing the richness and depth of designing, creating and experiencing visualisations in the physical world. Therefore, this thesis sets out to investigate the original and physical way of data visualisation with more physical and hands-on approaches.

1.2 CONTEXT

For information visualisation to provide access to data and information to a wider range of users, in particular non experts, remains a major concern (Samuel Huron et al., 2014). However, with a few notable exceptions, (Huron et al., 2014; Y. Jansen & Dragicevic, 2013; Jansen et al., 2013), little attention has been payed to the process of how people make sense of data and create visual- mapping using physicalisation. This design research aims to clarify how people use physicalisation to transform data into physical-visual representations and why in an age of digital content and data. This project focus on the design and making of physical and non-digital artefacts which form a collaborative

2 Chapter 1: Introduction

physicalisation system suitable for a broader range of audience independent of their level of expertise in visualisation, to achieve a higher level of democratisation and inclusion in the field of visualisation.

This project is also concerned with collaborative data physicalisation. A collaborative data physicalisation is a set of physical artefacts that enable users to map and visualise data together. Even though the practice-based outcome of this research study is non-digital and remains physical the design research and approach to this project lies at the intersection of various fields of research, which encompass tangible and embodied interaction design, physical data and information visualisation; data physicalisation, and collaborative tangible interface design. I will describe these terms in detail later in the literature review chapter, section 2.2.4 and in (Figure 11).

1.3 PURPOSES

This project aims to explore the design, creation and testing of a collaborative physicalisation system, and reports on its possibilities and limitations within a real context of use. Following a survey of existing literature on data physicalisations, a prototype data physicalisation for mapping household energy consuming activities was designed and fabricated. This prototype was then evaluated through a series of five in-situ evaluations with household members (two per evaluation) in order to evaluate the use of the data physicalisation and understand how users made use of it. A detailed analysis of the users’ physical interactions with the prototype was then undertaken, which informed the development of a series of themes of interaction, which capture some of the strategies and qualities of interaction that were exhibited.

This project contributes to understanding and exploring such an approach. The artefacts in the form of a collaborative physicalisation system designed in this project can be considered as an example to be replicated, changed, improved and tested with different datasets in different contexts of use. In this project, as a designer, I engage, observe and create through a process of reflective research and design. In summary, this project aims to produce a physical system that can be used to create physicalisations for a variety of data sets.

Chapter 1: Introduction 3

This project involves three research questions, listed below and summarised in (Table 1).

RQ1. What concepts from tangible interaction and information design literature can be applied to support collaborative physicalisation?

RQ2. How can a collaborative physicalisation system be designed and made?

RQ3. How do people employ data physicalisation as part of their collaborative sense-making and meaning-making?

Table 1. Research Questions, Methods and Outcomes

Research Questions Methods Collected Data/ Outcome

RQ1. What concepts from

tangible interaction and Literature Review information design literature Literature Survey can be applied to support Chapter 2 collaborative physicalisation?

RQ2. How can a collaborative Observation Design Development physicalisation system be Chapter 4 designed and made? Reflective design

Observation

RQ3. How do people employ Audio-Visual data physicalisation as part of Result and Analysis Design Experiment their collaborative sense- Chapter 5 making and meaning-making? Design Evaluation

Field Note

1.4 SIGNIFICANCE AND SCOPE

Visualisation2 can be defined as formation of mental images for raw data. Data visualisation could be considered an external representation, an external

2 Card defines information visualization as “The purpose of information visualization is to amplify cognitive performance, not just to create interesting pictures. Information visualizations should do for the mind what automobiles do for the feet.” (Card, 2008, p. 539)

4 Chapter 1: Introduction

compared to an internal mental understanding. Cognitive psychologists use the term external representations as a result of the interpretation of occurrence in the external physical world; these interpretations consist of spatial connections and can be differentiated from the internal cognitive model (Zhang & Norman, 1994). As a result, data physicalisation can provide an external physical skin for abstract data that can enhance this cognitive process. I should acknowledge there are many great works and contributions on the fields of tangible user interfaces TUI, tangible embedded and embodied interactions TEI, and another relevant field of human-computer interaction HCI, which this research plan to review and adopt the applicable concepts to support collaborative physicalisations. However, it seems the application of similar projects in the context of collaborative data physicalisations are very limited, and almost always they are as an add-on part of a digital application or solution.

To understand better and gain substantially from the physical characteristics of data physicalisation this project emphasised the design of the tangible and embodied interaction. I expand my definition and use of these terms in the literature review chapter in section 2.4.1. More specifically, the attention and focus of this study was on the design of the interaction and not the interface. Understanding data physicalisation requires a holistic appreciation of different levels of interactions. One level of interaction is constructed purely on the physicality of the object, the immediate interaction via haptic and visual senses and what one might be able to learn. In this project, the physical and non-digital interaction is the focus of designing embodied, and tangible interaction. Moreover, physicality and use of affordances of what the object offers are at the core of this design. In other words, it is about the physical nature of the object and the tangible and physical experience it can offer. A more profound level of interaction can be achieved through the use of the physical properties of the objects. This can facilitate deeper engagement by considering the affordances of the object (Vande Moere, 2008). This level of interaction can convey a meaning or communicate a concern, rather than merely showing the data (Vande Moere, 2008).

Chapter 1: Introduction 5

In this project, I argue that a shift in our approaches towards designing and making physicalisation is required—an approach in and from the ‘physical world’ for the ‘physical world’, which considers the natural physical quality and tangible interaction and design at its core. With the available technology, in particular, computer-aided applications and digital fabrication, we have a powerful tools to produce many different physical shapes and forms. This project uses these technologies and tools to create a collaborative physicalisation system and explore how people employ physicalisation as part of their collaborative sense- making and meaning-making.3

1.5 THESIS STRUCTURE

This thesis is comprised of six chapters. Chapter 1: Introduction, outlines the background (section 1.1), and context (section 1.2) of the research. Section 1.3, highlights the purposes and section 1.4, describes the significance and the scope of this research. Finally, section 1.5 includes an outline of this thesis structure.

Chapter 2: Literature Review, begins with an introduction and the role of digital fabrication in data physicalisation (section 2.1). The next section reviews the literatures on the field of data physicalisation (section 2.2), and continues with presenting influential concepts from the field of data and information visualisations that support collaborative physicalisation and the minimal set of requirements identified for information visualisation (section 2.3). Section 2.4, focuses on the theoretical perspectives from HCI and perspectives on collaboration from tangible interaction and interface design. Finally, section 2.5 provides a summary of the chapter.

Chapter 3: Design Research, describes the design research used in this study to address the main objectives identified earlier in chapter 1. Section 3.1 presents the methodology adopted in this study, and the design research. Section 3.2 introduces the participants. Section 3.3 lists all the tools and technology used

3 Measuring Cup by Mitchell Whitelaw and Building Visualisations with Tokens by Samuel Huron are two of the projects that have similar approach. Later in the literature review chapter I will cover different aspects from different projects and researches.

6 Chapter 1: Introduction

in this study and explains their use. Section 3.4 provides a summary of the methods used at the different phases of the study. Section 3.5 outlines how the data was analysed and section 3.6 presents a summary of ethical considerations and limitations.

Chapter 4: Design Development, provides a detailed report on the process of designing and making the physicalisation system. After the introduction (section 4.1), section 4.2 provides the detailed account of designing the system through iterations of form making, testing the system, data mapping, brain- storming and reflecting on design. Section 4.3, introduces the context of the use for the design evaluation and iterations of design and making the system to be tested by participants. Section 4.4, presents the design iterations for the final system and planning and designing the design evaluation activity sessions to test the final system with participants in a real-world scenario. Finally, section 4.5 provides a summary of the chapter.

Chapter 5: Results and Analysis, presents the results and analysis of participant interactions and engagements in the study settings. After the chapter’s introduction (section 5.1), section 5.2, provides an overview of the design evaluation activity sessions and formation of meta-qualities as a result of grouping the identified themes based on observations and analysing the collected data from each session. Section 5.3, presents each meta-quality and explains why each particular aspect is highlighted supported by examples from collected data. A summary of the chapter provided in (section 5.4).

Chapter 6: Discussion and Conclusion, consists of a discussion (section 6.1), which outlines the research questions in this study, the methods used to answer them, the outcomes of this thesis, and summarises the thesis findings. Section 6.2, contribution to knowledge highlights the importance and significance of the approach towards data physicalisation in this project and how it can contribute to knowledge. Section 6.3, limitations and future work, presents the limitations and recommendations for future work, as well as a discussion of where the study may be extended, and what can be the practical implications. Finally, section 6.4 presents the conclusion.

Chapter 1: Introduction 7

Chapter 2: Literature Review

This chapter begins with an introduction and the role of digital fabrication in data physicalisation (section 2.1). The next section reviews the literatures on the field of data physicalisation including a review of key exemplars (section 2.2), and continues with presenting influential concepts from the field of data and information visualisations that support collaborative physicalisation and the minimal set of requirements identified for information visualisation (section 2.3). Section 2.4 focuses on the theoretical perspectives from HCI and perspectives on collaboration from tangible interaction and interface design. Finally, section 2.5 provides a summary of the chapter.

Chapter overview This chapter surveys relevant research into data physicalisation, identifies a gap in the literature and provides a theoretical context and background for investigating the research topic. I first identify several different approaches to data physicalisation evident in the literature. From this survey, I highlight that the role that data physicalisations can play in supporting collaborative data analysis and sense-making is an area in need of further exploration. I then draw on existing theoretical perspectives from the field of interaction design to ground the research methodology, design, and analysis, which are described in the following chapters.

2.1 INTRODUCTION

This research is concerned with collaborative interactions around physical data visualisations. In this chapter, I will explore and review relevant work to this research topic to establish an understanding of a diverse range of projects relating to collaborative physical data and information visualisation physicalisation. This chapter aims to position the work presented by this research within the field by linking to relevant existing projects.

The primary purpose of visualisation is to assist us to understand data. Due to the advantages of our visual sense, we are greatly capable of seeing

Chapter 2: Literature Review 9

arrangements, movement and flow on bits and different forms (Manovich, 2011). Therefore, a strong visual representation could benefit our mental computation through a simple perceptual interface (Anderson, 2003; Bara et al., 2004). This could improve understanding, memorisation and decision making (Bara et al., 2004; Gwilt et al., 2012). However, more is possible. The visual presentation can target a larger group of users by making data and information more available and engaging for people to experience and explore. The past decade of research has seen a growing interest and body of research that investigates how data can be presented in forms beyond the digital (Amar et al., 2005; Anderson, 2003; Bara et al., 2004; Gwilt et al., 2012; Hornecker, 2011) – what I term data physicalisation. Physicalisation seems to promote more in-depth understanding and engagement both in the interaction between users and data-set and the relation between users (Bara et al., 2004; Hornecker, 2011). The emergence and higher availability of digital fabrication technologies alongside with CAD applications has led to more interest in data physicalisation.

Digital Fabrication and Data Physicalisation In the digital fabrication process, digital design files are transferred to computer-aided manufacturing devices to fabricate physical objects. Although digital fabrication technologies are growing and becoming more widely available, the majority of software running the fabrication machines specialises in static model making and does not support data-driven fabrication. In contrast, there is a wide selection of software to make screen-based visualisations, but this software does not support making physical objects. As a result, to design and craft such visualisation forms a complicated issue for which neither of these two communities has the perfect solution (Swaminathan et al., 2014).

The process of making an adequate graphical visualisation with an acceptable result is now available to the large group of users (Viegas et al., 2007), but we cannot claim the same for physicalisation. This is because of the absence of a proper conceptual model or the reality that most of the attempts towards physicalisation copy the approaches from existing traditional graphical visualisations (Swaminathan et al., 2014).

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The increased availability of digital fabrication technology is reducing the time and effort required for manufacturing physical presentations (Yvonne Jansen et al., 2013). Some examples of static data physicalisation produced by digital fabrication can be found below in the static visualisations section. To name a few: Measuring Cup by Mitchell Whitelaw (Figure 3.b), Data Necklace of Goodnight SMS by Paul Heinicker (Figure 5.a) and the author’s previous work, DNA Ring (Figure 5.c).

Even though there are various digital fabrication machines, mostly there are two dominant techniques for digital fabrication. Subtractive approaches (laser cutting, CNC milling) that remove material. Additive approaches (3D printing) that add to the material layer by layer. One of the primary concerns when fabricating an object is to embody specific physical properties including manufacturability, connections and joints, balance and robustness (Swaminathan et al., 2014). As a part of the design process, this study will employ and reflect on both additive and subtractive fabrication techniques. Furthermore, there is a potential for unique new forms to be created as a result of using these manufacturing technologies.

2.2 SURVEYING THE FIELD OF DATA PHYSICALISATION

Through data physicalisation, we map data and information to physical matter rather than pixels. In this section, I will investigate some of the examples and highlight characteristics relevant to this project. These examples cover a wide range of projects including historical data physicalisations, data sculptures, interactive and collaborative data physicalisations, data physicalisations based on personal data sets and data physicalisations that use concrete scales and metaphors.

This section also covers some of the characteristics of data physicalisation aligned with the goal of this research, including the minimal set of requirements for information visualisation, constructive visualisations, and democratisation.

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Historical Data Physicalisations Data physicalisations have been employed since the prehistoric era, prior to the invention of writing. The clay tokens in (Figure 1.a) are an important example of such an approach.

Figure 1.a. Plain tokens, b. Complex tokens.

The clay tokens appeared in western Asia more than 5000 years ago (Schmandt-Besserat, 2009). Mesopotamia Plain tokens (Figure 1.a.) appeared more than 4000 years ago in the area where Iraq is located today. The cone, spheres and disk represented various grain measures. Complex tokens (Figure 1.b.) appeared more than 3300 years ago in the area where Iran is located today. The clay tokens represent, from left to right, one garment, one ingot of metal, one jar of oil, and one sheep.

Another historical example (but one that is closer to our time) is the physical visualisation of power consumption by Detroit Edison Company in the 1930s (Figure 2).

Figure 2. 1930 Detroit Edison Company Physical Visualisation

This data physicalisation aimed to improve the prediction of power demands. It was one of the earlier examples to aid goal-oriented productivity tasks. This physicalisation was built from a series of slices of data with one slice per day, where every day was divided into 30-minute intervals (Brinton, 1939).

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Data Sculptures Data sculpture is a form of data physicalisation which can obtain both pragmatic and artistic qualities. Zhao and Vande Moere (2008) argued that data sculpture could be considered as an alternate method of communicating information to ordinary audiences. Furthermore, based on Vande More’s hypothesis on data sculpture, information can be understood and presented by physical objects that can be held, manipulated, examined, studied, or even worn or owned (Vande Moere, 2008). Therefore, a data sculpture performs as an externalisation of data, as it seeks to engage the creativity and analytical abilities of its audience by both its functional and artistic characteristics. Data sculpture can convey data-related perceptions indirectly and promote social and cultural impacts on audiences. The physical qualities of data sculpture bridge people’s comprehension and their experience in the physical world (Zhao & Moere, 2008) to support the democratisation of information.

Externalisation and democratisation are two important concepts in this study which I will further explain later in this chapter and throughout this thesis. Next, I will present some examples of data sculptures. The importance of pairing the physical representation and digital data seems to be a pivotal manifestation of the data sculptures. Data sculptures can be artistic, static (Figure 3), pragmatic or dynamic (Figure 4).

Figure 3.a. Keyboard Frequency Sculpture, b. Measuring Cup, c. Sweat-Atoms

Knuepfel created a memorable visual-memory for audiences by designing the Keyboard Frequency Sculpture (Figure 3.a). He made a physicalisation in the form of static data sculpture based on the frequency of use of alphabet keys on a keyboard; his design escalates each key according to its repetition. Thus, E is the tallest, followed by T and A; the letter Z is the shortest (Knuepfel, 2011).

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Measuring Cup is a static data sculpture of weather data. Whitelaw designed, and 3D printed this data physicalisation in the form of a cup-shaped object. The shape consists of horizontal layers which represent 150 years of Sydney temperature from 1859 and based on monthly temperature. Whitelaw describes Measuring Cup as “a moment of haptic tension between human-centred pleasure and the evidence of how our human-centeredness is playing out for the planet as a whole” (Figure 3.b). The shape of the final form is perfect for a cup, and the widening of the shape towards the lip is a result of global warming (Whitelaw, 2009).

In another project, Khot and his colleagues at RMIT created a set of static data sculptures based on participants’ physical activity (Figure 3.c). Sweat Atoms converted physical-activities data sourced from heart rate monitors into 3D printed physicalisations (Khot et al., 2014). The project aimed to learn what data sculptures can offer to understand physical activities. Some of the findings of the project indicated that participants were more aware of their engagement with physical activity and demonstrated various degrees of involvement with data sculptures.

The research team studied six households, where participants were tested, and experienced five different data sculptures based on their physical activities’ data for two weeks. All five data sculptures for Sweat Atoms were 3D printed (Figure 3.c) and from left to right included 1. The Heartbeats Graph, the 3D printed graph of a participant’s heart rate per minute. 2. The Flower shape, in which the floral pattern reflects variations in the heart rate and the length of petals denotes heart rate intensity. 3. The Frog, in which the size of the frog indicates the amount of physical activity. 4. The Dice, in which each side indicates the zone-based analysis of heart rate data. 5. The Ring, in which the number of the bubbles reflects the number of active hours and the diameter of the bubble shows the amount of physical activity an hour. This project motivated designers to consider different approaches provided by interactive technologies available in particular digital fabrication to support user’s experience (Khot et al., 2014).

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Dynamic Data Sculptures Some data sculptures are dynamic; these are usually based on data sets manipulated independently from users’ interaction directly with the physical sculptures, and mostly through a digital application.

Figure 4.a. Centograph, b. Emoto, c. eCLOUD

Centograph is a dynamic data sculpture that provides an opportunity for users to observe changes over time in regards to the specific news topics, from science and technology to art and entertainment (Figure 4.a). This data physicalisation shows keyword popularity developed by Tinker, a London-based company (‘Centograph’ - Tinker London, 2009). This physical bar chart was made of 10 adjustable acrylic bars. Each bar represents a decade worth of data and indicates the popularity of the searched term. Centograph is connected to an online application which searches the Google News Archive for the chosen keywords’ repetition for related news articles over the past century (from 1909 to 2009) and adjusts the bars’ heights accordingly (‘Centograph’ - Tinker London, 2009).

Emoto collected worldwide Tweeted feeds online around the London 2012 Olympics to create a data sculpture with a dynamic projected heat-map (Figure 4.b). This data physicalisation represents the number of messages collected per hour, and the sentiment level in horizontal lines which move up and down per the current number of tweets at a time. Emoto consists of 17 physical pieces, each representing all tweets collected during one day of the 2012 Olympic, from day 1 to day 17. In addition to the data sculpture, Emoto has a dynamic heat-map projected on to the objects for the most appealing and entertaining themes extracted from the Twitter data and identified by designers. Audiences were able to go through the themes using an interactive controller to explore the archive and survey the general emotional vibes of the Olympics over time. This project

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was integrated with an online visualisation that ran live during the Games in July and August 2012. The project was documented at the We Play closing exhibition of the Cultural Olympiad in Northwest London (Hemment & Stefaner, 2012).

eCloud is a dynamic data sculpture built of many large LCD pixels arranged in a 3D physical space (Figure 4.c). eCloud is capable of changing an LCD’s opacity according to real-time weather data from different cities around the world. Beneath the installation, a monitor shows the current city weather data and visualisation mapped to the LCDs. This project was designed for and installed at the San Jose International Airport in California (Goods et al., 2010).

Data Physicalisations Based on Personal Data-Sets There are different ways of making more engaging and meaningful data physicalisations. Some physicalisations come in the form of wearables and use personal data sets to create a more personal bond with users. Miller’s study found that people like to show their emotions with physical artefacts that reflect their lives, accomplishments, feelings and personalities (Miller, 2008). This relates to the concept of “autotopography”, defined as an arrangement of material artefacts as physical signs that spatially represent the identity of individuals (González, 1995; Khot, Hjorth, & Mueller, 2014b, p. 3842). From a collaborative perspective, it seems that personal data support the idea of externalisation better. Later in this chapter, I will explain the concept of externalisation in more detail. Next, I will present some examples of personalised physicalisations.

Figure 5.a. Data Necklace, b. Meshu, c. DNA Jewellery

Goodnight SMS is a good example of a simple yet meaningful and rich data physicalisation (Figure 5.a). Heinicker designed this project based on “good night” SMS messages between him and his girlfriend over a period of two years. This static and artistic physicalisation shaped and 3D printed a necklace

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representing a long-distance relationship. Heinicker chose a simple rectangle shape to map the data. Two diagonally opposite corners of the rectangle represent the beginning of each year; the continuous vertical sides were mapped based on days with and without a good night SMS and demonstrated the length of each SMS over the applied line graph. The flat horizontal sides indicated days spent together (Heinicker, 2015).

Meshu is a project that enables users to create personalised accessories based on the cities they choose on a world map (Figure 5.b). Binx and Hwang started Meshu in 2012 as an online business, using 3D printing technology to create data physicalisations. The locations chosen by users on the online map available at the Meshu web page are linked together and create a line drawing pattern for a physical mesh which is then fabricated by using 3D printing technology (‘Meshu’ - Binx & Hwang, 2012). This static and artistic data physicalisation is another example that supports using a personal data set to create a meaningful and engaging physicalisation.

DNA Jewellery was a project with an iterative design process to design and create a data physicalisation that could map standard DNA profiling data in the form of a wearable (Figure 5.c). In this project, I explored design concerns with the making of data physicalisations and highlighted the significance of the approach that moves between art, technology, design and visualisation (Rezaeian & Donovan, 2014).

Often most of the physical data visualisations are limited to the artistic category (Kosara, 2007); this includes most data sculpture examples and recent examples based on personalised data presented so far in this review: Data Necklace, Meshu.IO and also Data Jewels by Christoph Zellweger (Zellweger, 2007).

Figure 6.a. Bigger DNA JEWELLERY 3D printed in plastic, b. Smaller DNA JEWELLERY 3D printed in silver; It is much easier to read the data from the bigger artefacts 3D printed in plastic (Figure 6.a) compared to the much smaller artefact 3D printed in silver (Figure 6.b).

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Similar to DNA Jewellery in the Data Jewels project, digital fabrication and CAD applications were used to design and make jewellery based on personal genetic code. However, with the DNA Jewellery project, I tried to maintain the requirements for pragmatic visualisations and physicalisations. Most importantly for data readability4, DNA Jewellery requires some training to learn how to read the data from the physical artefact, and the scale of the artefact affects the readability (Figure 6).

Data Physicalisations Using Concrete Scale and Metaphors Another method to create a more meaningful and engaging data physicalisation is to use concrete scales and metaphoric values, which is a common approach to comprehend complex measurements. The concrete scale uses relevant and meaningful units which are more familiar to users, making it easier for them to understand (Chevalier et al., 2013).

Figure 7.a. How Much Sugar Do You Consume: Amount of sugar in different size Coke, b. Can We Keep Up? Sponges map, c. Of All the People in the World

How Much Sugar Do You Consume? is an excellent example of concrete scale visualisation, using actual objects to visualise data and information (Figure 7.a). Sugar Stacks and Rethink your Drink are data physicalisation projects that use actual objects to represent complex measures. Every time you consume a can of Coke (355 ml) the nutrition label tells you, you ingest 39g of sugar. However, what does that much sugar look like? The purpose of these projects is to improve consumers’ knowledge and understanding of their food and drink intake. To answer the question above about a can of Coke, each cube of sugar contains four

4 Later in this chapter, under Minimal Set of Requirements for Information Visualisation I will use Kosara’s work to explain the minimal set of requirements and differences between artistic and pragmatic visualisations.

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grams of sugar. Therefore, a can of Coke contains almost ten cubes of sugar (9.75 cubes to be exact) (Chevalier et al., 2013).

The Can We Keep Up? project aims to support and create a meaningful and metaphoric data physicalisation to influence audiences regarding the topic of the need for water in cities around the world (Figure 7.b). The designers Matt and Hal Watts, inspired by the little sponges in a soldering iron set, realised how individually each expanded sponge could be a suitable approach to present water demands on a map. Thus, he made a physical map by carving the form of each country out of compressed cellulose sponges and deliberately added an explicit volume of water to each piece. The spongy presentation supports a meaningful and metaphoric physical visualisation, with the ambition to engage audiences regarding the topic (‘Can We Keep Up?’ - Laws & Bennett, 2011).

In the Of All the People in the World project, every single grain of rice represents a person. The grains are organised in labelled piles, forming a data landscape of rice (Figure 7.c). Each pile represents a different data set; some of the chosen data sets include the populations of towns and cities, the number of births and deaths on a daily basis and all the people who have walked on the moon. Stan's Café, the British group of artists, started a series of data physicalisations in 2004 and had showcased their work in many places around the world. The audience can compare one grain representing each individual to the other grains in each physicalisation. The theme and size of the presentation change from one exhibition to another depending on the venue, city or country hosting the show. During each demonstration over the course of a few days, a team of performers uses delicately scaled quantities of rice to represent a number of humans in a chosen set of statistical categories (‘Of All The People In All The World’ - Stan’s Cafe, 2004).

Even though the data physicalisations presented above are engaging in many ways and have some of the essential characteristics to support collaboration, most of them have not been tested or evaluated for collaborative use. I use ‘most’ because I assume the historical example of clay tokens offers suitable objects to support collaborations. Next, I will present some of the interactive and collaborative data physicalisations.

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Interactive Data Physicalisations Interactive data physicalisations, unlike dynamic data sculptures, support direct manipulation between users and the physical object. Direct manipulation is an important aspect from the perspective of this project, as it supports collaboration. Later in this chapter, I will explain the importance of this characteristic for this project.

Figure 8.a. From over here, b. Inverted collaborative bar charts, c. MakerVis

From Over Here is a data physicalisation made of laser-cut pieces of cardboard, each indicating reports regarding or linked to ‘Ireland’ from the New York Times since 1992 to 2010 on a monthly basis (Figure 8.a). The Irish designer Paul May designed each month’s card to be different in length depending on the number of articles from that month; all the people and news titles mentioned in the articles are engraved on each laser-cut piece. From Over Here supports direct manipulation by the audience; in this project, the user can move each card manually to read, compare and explore (‘From Over Here’ - May, 2011).

Inverted Physical Bar Charts is a good example of public, anonymous, friendly and playful data physicalisation (Figure 8.b). In a joint project with a sociologist, Andrew Barry created a physicalisation that supports direct manipulation. In this project, the audiences were invited to choose tokens from large transparent tubes which contain tokens in the form of a badge with a unique message according to the theme of the exhibition. The lower level of each bar indicates the more popular badges. In addition to the tubes and badges, there are postcards that ask participants to predict the levels in the tubes in advance (‘PhysicalBarCharts’ - Kimbell, 2005). This data physicalisation was first exposed to audiences in the Day to Day Data exhibition at Angel Row Gallery, Nottingham in 2005. At this exhibition, visitors were asked; What did you do during the week prior to the show? The badges included following massages: “I spoke up, I helped

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someone, I got by, I made a stand, I did nothing” (‘PhysicalBarCharts’ - Kimbell, 2005).

MakerVis is a data physicalisation approach that allows users to accomplish different information visualisation assignments by direct physical manipulation (Figure 8.c). In this project, Jansen designed a prototype that develops a complete procedure of making a data physicalisation, from data filtering to physical fabrication. Each layered physical visualisation is interchangeable and provides an opportunity for users to create physical line-charts, bar-charts, prism maps and scatterplots (Jansen & Dragicevic, 2013).

The data physicalisations in this project present the evolution of country indicators over time. An empirical study by Jansen found that a rearrangeable physical bar chart outperformed a matching digital on-screen version regarding data searching tasks (Jansen et al., 2013). Jansen’s findings highlight that, in addition to communicational, educational and artistic approaches, physical visualisations could accomplish more substantial information visualisation tasks. This capability appears to originate within the characteristics that are exclusive to physical items, such as their quality to be handled physically. Furthermore, this project encourages more empirical research on reconfigurable systems and digital fabrication as a tool for making such systems.

Data Physicalisations and Collaboration In this section, I will discuss some of the physicalisation projects that support collaboration.

Figure 9.a. General Motors’ 3D LEGO Visualisations, b. Building visualisations with tokens

The General Motors’ 3D LEGO Visualisations is a great data physicalisation example to support collaboration in a group. Herrick, the global chief engineer at General Motors at the time, highlighted some of the positive impacts when teams

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got together to update the board during their regular meetings. He explained that teams at General Motors found Lego mapping very transparent, straightforward and fun to use (Wilson & Wilson, 2012). Herrick built this data physicalisation by using Lego bricks to visualise production issues (Figure 9.a). Also, to complete a paper report as a usual practice, they also mapped the issue to the Lego board. On the Lego board, different colours represented different areas of the automobile, and the size of the bricks indicated the gravity of the issue. Using data physicalisation, the company employees could see more clearly how significant the problems were and where it was suitable to address them in the grand scheme (Wilson & Wilson, 2012).

Building Visualisations with Tokens is a data physicalisation project that supports the democratisation of visualisation and constructive visualisation. Democratisation means providing a way for everyone, including lay users, to make visualisations. Later in this chapter, I will explain this concept in more detail and expand on how it can support collaboration. Constructive visualisation is a paradigm for creating simple, adjustable and changeable visualisations. I will also explain this paradigm in more detail later. In his study Building Visualisations with Tokens, Huron demonstrated how his participants made visualisations by using physical tokens. Participants were asked to build, update and present their information by using physical tokens only (Figure 9.b). Considering most of the users had limited knowledge in regards to visualisation authoring, they found it easier to make and talk about their visualisations (Huron, et al., 2014).

2.3 CONCEPTS FROM DATA AND INFORMATION VISUALISATIONS

There are many old and new examples of data physicalisations, and each is unique in the way it was designed and made and how it functions. Consequently, we can group physicalisations in many different ways. In this section, I will look at the specific characteristics highlighted so far in this chapter and explain how they support the collaborative aspect of data physicalisations and are relevant to this study. These characteristics are namely, democratisation, constructive visualisation, direct manipulation and externalisation. First, I will cover democratisation and constructive visualisation. Then I will look at Kosara’s work on the Minimal Set of Requirements for Information Visualisation (Kosara, 2007)

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to present the minimal set of requirements for data physicalisation. In the next section, I will explain direct manipulation and externalisation and introduce other characteristics from Human Computer Interaction (HCI) that support collaboration.

Democratisation Democratisation is essential to this project since it supports the idea of empowering lay users to create their visualisations. Democratisation can be supportive of other characteristics beneficial to collaboration, including simple interaction with an artefact, natural interpersonal interactions among users, and fluid transition (Hornecker, 2002). The democratisation of visualisation or physicalisation enables lay users and not only professionals to design, debate and publish their data (Huron et al., 2014). To provide a tool that does not require a great deal of particular expertise, such as coding or programming, or a long learning cycle and mastery of specific skills, such as illustrations and visualisation, is key to democratisation. Some traditional visualisation projects address this by designing an easy to use tool, for example, the Many Eyes Project, which is an online platform that makes it possible for a typical online user to create interactive visualisations (Viegas et al., 2007).

Constructive Visualisation A constructive visualisation should be a visualisation approach that can support democratisation. A constructive visualisation is simple to make and easy to manipulate for everyone including non-experts. Skills required to interact with constructive visualisations are basic; similar to the skills required for young kids at the playground, which builds on our inherent comprehension and memory from the physical world. It is “expressive” and “dynamic”; one can make, reassemble and modify such visualisation within the designed constraint of the environment (Huron et al., 2014, p. 433). Furthermore, the constructive approach enables users to interact directly with datasets; this quality can support direct manipulation and simple interaction, the two characteristics identified by Hornecker as beneficial for collaboration (Hornecker, 2002). Constructive visualisations are dynamic and interactive in the way they can be rebuilt and adjusted, and this is a unique characteristic of physicalisation to support

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democratisation. Constructive physicalisations are suitable for collaboration to facilitate discussion and make it easy enough for everyone to share and demonstrate their ideas. A constructive visualisation builds upon our inherent cognition and experiences with the physical world, offering an approach that empowers lay users to create a unique and original visualisation and an ability to interact and engage with data sets and each other (Huron et al., 2014).

Minimal Set of Requirements for Information Visualisation Kosara argued that the classification of visualisations typically draws on technical criteria, leaving out an artistic approach: “Understanding the differences between information visualisation and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches.” (Kosara, 2007, p. 1). According to Kosara, there are two primary schools of thought which divide visualisation approaches into two corresponding groups. On the one hand, technical, pragmatic or analysis-oriented visualisations and on the other hand, artistic approaches (Figure 10) The Parallel Sets is a pragmatic visualisation (Figure 10.a) that shows data concerning the passengers on the Titanic and MilkDrop is an artistic visualisation (Figure 10.b.) which is a music visualisation driven by data (Kosara, 2007).

Figure 10.a. Parallel Sets show data about the people on the Titanic which is readable and recognisable as a visualisation b. MilkDrop is Music visualisation which is data driven, but not readable

Kosara proposed that since information visualisation is such a naturally cross-disciplinary field, it often demands a hybrid approach. He recommended a logical starting point for a more interdisciplinary approach is to find an alternative way of visualising information that combines ideas from both pragmatic and artistic approaches. He also indicated that a significant portion of the uncertainty concerning visualisation comes from the reality that there is no

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explicit definition of visualisation that is accepted by the majority of researchers, practitioners and users. He emphasised the requirement for a consistent definition at least inside each field (like HCI, design, computer science and so forth). As a result, Kosara considered the three criteria to be a minimal set of requirements for any visualisation: 1. Visualisation is based on (non-visual)5 data, 2. Visualisation produces an image and 3. The result of visualisation should be readable and recognisable6 (Table 2), (Kosara, 2007).

Table 2. The Minimal set of requirements for any visualisation by Kosara

Minimal Set of Requirements for Information Visualisation

1 Is based on (non-visual) data

2 Produce an image

3 The result should be readable and recognisable as a visualisation

Although Kosara’s approach mainly relates to screen-based information visualisations, I consider data and information physicalisation to be aligned with Kosara’s hybrid approach towards visualisation. Thus, in this project, I adopt the minimal set of requirements introduced by Kosara as a minimal set of requirements for any data physicalisation.

It is critical to acknowledge that the third criterion from the minimal set of requirements by Kosara - Readability and Recognisability of the Result - is particularly important. This criterion is missing from most artistic visualisations, and perhaps the readability and recognisability of the result is the most significant and apparent difference between pragmatic and artistic visualisations.

5 According to Kosara “Visualization is not image processing or photography; if the source data is an image and is used as an image in the result, it is not being visualized.” (Kosara, 2007, p. 2) 6 It should be acknowledged the notion of readability and recognisability defined by Kosara may be culture and audience specific, but they nevertheless do provide a useful starting point for forming a minimal set of requirements for any visualisation. According to Kosara “There are many ways to transform data into images, most of which do not allow the viewer to understand the underlying data. A visualization must produce images that are readable by a viewer, even if that requires training and practice. Visualization images must also be recognizable as such.” (Kosara, 2007, p. 2)

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As this chapter and the presented data physicalisations examples show, most data physicalisations, in particular, data sculptures like artistic visualisations, fall into the group of visualisations which are not readable and recognisable (Vande Moere, 2008). The readability and recognisability of the result for data physicalisation remain a challenge that I aim to address in this research project.

2.4 THEORETICAL PERSPECTIVES FROM HCI

As I highlighted earlier, this project is concerned with collaborative data physicalisation, which is at the intersection of various fields of research, including tangible and embodied interaction (Interaction Design), data physicalisation (Data and Information Visualisation) and collaborative systems design (HCI)(Figure 11). In this section, I will introduce some of the theoretical concepts that I have found useful to support for this project, namely, embodied interaction and affordances, boundary objects and tokens and constraints. As well as perspectives on collaboration from tangible interaction and interface design.

Embodied Interaction and Affordances From studying different physicalisation examples and their characteristics, one perspective can be: to achieve a suitable design for collaborative data physicalisation, collaboration and physicality must be part of the design and interaction. This can be informed by an ‘embodied interaction’ approach, as physicality is an essential characteristic of embodied interaction (Hornecker, 2011). More specifically, the attention and focus should be on the design of the interaction and not the interface. This means foregrounding attention to interaction dynamics and qualities (Hornecker & Buur, 2006).

Understanding data physicalisation requires a holistic appreciation of different levels of interactions. One level of interaction is constructed purely on the physicality of the object, the immediate interaction via haptic and visual senses and what one might be able to learn. In other words, it is about the tactile nature of the object and the tangible and physical experience it can offer which is also aligned with the direct manipulation requirement as one of the identified characteristics for designing a collaborative physicalisation (Table 7). It is also

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important to keep in mind that there are physicalisations with moving pieces. This characteristic can provide the basic quality for interactivity but not necessarily the kind of rich interaction investigated in this project to convey meaning. Almost all the examples from dynamic data sculptures mentioned earlier fall under this category. For example, the From Over Here (Figure 8.a) project supports physical interaction with each card but is limited to a physical movement to pull each card from the rest to be able to read it (May, 2011). Here the physical interaction does not offer much more than a physical movement. A more profound level of interaction can be achieved through the use of the physical properties of the objects. This level of interaction can convey a meaning or communicate a concern, rather than merely showing the data (Vande Moere, 2008).

The Affordances are what an object provides or offers the user. The word affordance was coined by James Gibson to refer to both the environment and the user to the extent that no other phrase does. It indicates the interdependent connection between the user and the environment. As a result, it is important to consider the design for affordances of the artefacts used in the research as they can provide information required to users; indicating how design artefacts function (Gibson, 1979; Shaw & Bransford, 1977).

The phrase affordance refers to the physical, natural and inherent properties of a thing, particularly those basic properties that influence and suggest how the thing could be used. There are two different kinds of affordances, perceived affordances and real ones. Both act very differently in physical products and physical world compared to screen-based products and environments. Each different type of affordance can be manipulated independently of one another. “Physical manipulation of objects is where the power of real and perceived affordances lies” (Norman, 1999, p. 41). Norman highlights “Today’s design often lies in the virtual world, where depiction stands in for reality. Many aspects of physical affordances are denied the designer: the alternatives are constraints and conventions. These are powerful when used well. I believe that our reliance on abstract representations and actions is a mistake and that people would be better

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served if we would return to control through physical objects” (Norman, 1999, p. 41).

Since the physicality of the objects and interaction constitute an inevitable part of this project, affordances play a crucial role. Hence paying attention to designing each piece could inform how users interact or use the system to create their data physicalisation. Supporting social interaction and collaboration is also one of the most substantial and domain-independent characteristics of tangible and physical interactions. This quality can be referred to as social affordances or collaborative affordances (Hornecker & Buur, 2006), and this is where we can consider how to address all the required characteristics identified earlier to design a collaborative data physicalisation.

Boundary Objects Boundary objects are objects with the flexibility to adjust to the specific and confined requirements of different places; at the same time, they are robust and solid enough to hold an identity across the different sites where they are employed (Star & Griesemer, 1989). Boundary objects can be concrete and/or abstract at the same time. They can carry a various meaning in various social environments and settings; their form and shape are ordinary so that they can be recognised across different environments. As a result, they can be a translation tool between different social contexts and support collaboration (Star & Griesemer, 1989).

The ideal type of boundary object is like an atlas or diagram or any description that does not explain the details of any site or object and is instead vague but adaptable and can be customised to describe a site in detail. It can communicate the general ideas of different parties. Boundary objects allow different parties from different groups to interpret different meanings from the same material according to their specific requirements. This is possible due to the flexible nature of boundary objects in group use and being more focused at the individual level. It is noteworthy that different forms and levels of nonverbal knowledge are obtained through interaction with boundary objects (Hollan et al., 2000). Boundary objects have the capacity to be changed or corrected by parties other than the ones who created or assembled them (Star & Griesemer, 1989).

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These characteristics make boundary objects suitable to support externalisation and democratisation and, as a result, collaborative data physicalisation.

Token and Constraint This project intends to design and make a collaborative data physicalisation based on the Token and Constraint concept. Physicalisation activity can be defined as participants interaction with the designed token and constraint system. To interact physically and arrange the tokens spatially in regard to the designed constraint and other tokens.

Chosen by many researchers and designers in the field of HCI to design and make interactive, tangible and physical system appear to have been referenced to the papers by Ishii et al. (Lefeuvre et al., 2018). Also, Shaer and Hornecker in their survey on “Tangible User Interfaces: Past, Present, and Future Directions” highlighted the importance of the work by Ullmer et al. (Shaer & Hornecker, 2010).

By placement of collaborative data physicalisation at the intersection of Tangible Interactions, Data Physicalisation and Collaborative Tangible Interface Design, it seems natural that the taxonomy on this project references and shares some of the concepts introduced by Ullmer and Ishii “Emerging Frameworks for Tangible User Interfaces” (Ullmer & Ishii, 2000), Scott et al. “System Guidelines for Co-located, Collaborative Work on a Tabletop Display” (Scott et al., 2003), and Hornecker and Buur, “Getting a Grip on Tangible Interaction: A Framework on Physical Space and Social Interaction” (Hornecker & Buur, 2006). These researchers provided some of the initial frameworks and guidelines within the specified field.

In the following I describe how the Token and Constraint paradigm based on Ullmer, Ishii and Jacob works can inform the design of a collaborative data physicalisation. (Ullmer et al., 2005)

According to Ullmer et. al. Tokens are single physical units that represent abstract data or information (Ullmer et al., 2005). Tobias et al. describes tokens as objects tied to the information they represent (Tobias et al., 2015). Arif et al. also refers to Ullmer et al. definition of tangible tokens as “discrete, spatially

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reconfigurable physical objects that typically represent digital information” (Arif et al., 2017, p. 481). Constraints are forms which mechanically confine how tokens can be manipulated into a variety of interpretations (Ullmer et al., 2005). Mora et al. refer to constraints as visual or physical confines with constrained areas that are resolute by physical or visual boundaries (Mora et al., 2015).

Token and constraint systems have two fundamental elements: First, tokens or physical objects as a representation of data and information and second, constraint or physical manipulations of the tokens as an interactively engaging meaning-making process (Ullmer et al., 2005). Lefeuvre et al. support this concept by referring to tangible tokens as containers “to hold other un- tangible artefacts, like data” (Lefeuvre et al., 2018, p. 490).

Token and constraint systems are generally deployed to interact with abstract data and information; as raw data in its original form has no inherent physical representation, nor a natural or physical vocabulary for its representation. Token and constraint systems can support analytical tasks efficiently in the field of physical interfaces. Therefore, presentation of data through systems designed based on tokens and constraints can frequently gain from the physical presentations of the tokens as well as the benefits of analytical augmentation of constraints. Physical tokens are deployed to characterise data and information. Physical constraints are deployed to map configurations of associated tokens into an array of interpretations (Ullmer et al., 2005).

The terminology “token” and “constraint” is used to demonstrate the strong interconnected link between the two components. Tokens and constraints should be united to create meaningful expressions; this includes both operators and operands, notably while tokens and constraints are physically apart, their physical integrable forms assist to show possible combinations and different usage scenarios passively. Constraints are manifested as physical formations which mechanically route how their associated tokens can be manipulated within their boundaries; restricting the movement of each discrete associated tokens to the limited physical degree of freedom (Mora et al., 2015; Ullmer et al., 2005)

Token and constraint systems have two different aspects of interaction, “associate” and “manipulate” (Ullmer et al., 2005, p. 84). In the associate aspect,

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an individual or a few tokens are associated with a particular constraint configuration. Association can be achieved by locating the token within the physical space provided by a constraint. This action may be undone by removing the token. The associate aspect forms a physical relationship between a token and a constraint. Often token and constraint systems support the associate aspect and only some systems support both the associate and manipulate aspects. In the manipulate phase, the token is physically manipulated within the confines of the constraint. In this scenario, tokens are constrained mechanically to be manipulated with a limited degree of freedom either across a straight axis or turnabout on a circular axis (Ullmer et al., 2005). By considering these two fundamental aspects it is possible to map physical interactions into analytical and/or computational operations. For instance, existence or non-existence of a token in an associated constraint can represent the binary values of zero and one (Mora et al., 2015).

The token and constraint connection can also be “nested” (Ullmer et al., 2005, p. 85). The physical object can perform as a source constraint for one or some associated tokens and at the same time as a child token inside a more significant constraint.

Tokens can also be moved and relocated between constraints to convey a different meaning. Sometimes constraints include different kinds of physical tokens. In these cases, the absolute and relative physical location and arrangement of tokens, both in relation to the constraint and to one another, can be mapped to different interpretations. From a slightly different view, token and constraint systems and interfaces can perform as a simple physical language, making it possible for an unrestricted mixture of physically manifested “operations” and “operands” (Ullmer et al., 2005, p. 85). This is where Mora et al. highlights token and constraint frameworks suitable for “providing a powerful descriptive language” (Mora et al., 2015, p. 143).

Token and constraint systems are often deployed to manifest interactive work-environments where physical interactions and actions lead to an instant understanding and interpretation as a result of the response by the system. In this regard, the token and constraint system’s behaviour intently follow the

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principles of direct-manipulation expressed in Shneiderman’s work (Shneiderman, 1983).

A fundamental aspect of token and constraint interfaces and systems is that they provide a physical form to both abstract data and information itself as well as to their syntax for manipulation. Syntax can be defined as, “the order and arrangement of the words or symbols forming a logical sentence.” (Ullmer et al., 2005, p. 86). It is in the grammar of the process that objects can be integrated to configure expressions and definitions which can convey meaningful interpretation by users. When positioned in a constraint, tokens are usually deployed to represent both as the container for a bulk of information, and at the same time as the control for interacting with them. Token and constraint interfaces physically communicate and demonstrate to users regarding the type of interactions the interface can (and cannot) offer and support.

Common Examples of Mappings The use of “racks” is a persistent form of constraints mapping; in this approach physical tokens are manipulated within a “linear” constraint (Ullmer et al., 2005, p. 87). The racks formations are the result of combining various fundamental physical characteristics. Notably, these linear arrangements can be explained regarding the absolute and relative positions of tokens to one another and the constraint.

This concept drives from ideas based on spatial theories from different disciplines such as psychology, artificial intelligence and linguistics that argues relevant concepts regarding primary and reference objects, as well as reference frames (Retz-Schmidt, 1988). In synopsis, the physical connections among tokens and constraints can be listed as four fundamental relationships as below (Ullmer et al., 2005):

 The absolute arrangement of tokens concerning one another

 The relative arrangement of tokens concerning one another

 The absolute arrangement of the token(s) concerning the constraint

 The relative arrangement of the token(s) concerning the constraint

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These conceptual physical connections can be mapped onto a number of particular interpretations.

Some of the Advantages of the Token and Constraint Approach It is beneficial to summarise some of the advantages of the token and constraint systems. Sometimes, our considerations should contemplate the possible gains or aims that potentially won’t be apparent often and may gain from observational or experimental confirmation.

Also, it is crucial to highlight the physical connections and syntax mentioned above are not restricted to token and constraint systems. For instance, similar relationships are observable and can be demonstrated among interactive interfaces. However, in comparison with interactive surfaces, the deployment of physical constraints promotes several advantages listed below (Ullmer et al., 2005):

 Elevated passive haptic response

 Elevated possibilities for active force response

 Reduced necessities for visual observation

 Elevated kinaesthetic recognition and understanding

 Elevated possibilities for embedded uses

A considerable part of these gains arises from the types of physical manifestation used by the token and constraint system. In particular, the deployment of physically embodied, mechanically restricting constraints assists to demonstrate (Ullmer et al., 2005):

 The mechanical formation of constraints can assist to demonstrate the physical relationship with their tokens. As they are often encrypted into the physical affordances such as shape and size.

 Physical tokens are generally mechanically limited to specific configurations that have clear and unambiguous interpretations.

 The precise boundary among interaction areas with different interpretations.

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The clear confines between constraints provide the support to integrate and merge different constraints, with diverse characteristics. Also, these boundaries help with the formation of constraints into the creation of self-contained interfaces.

Furthermore, to some extent from different standpoints, the deployment of physical constraints can support other advantages such as (Ullmer et al., 2005):

 Human perception. Physical constraints use physical affordances to encode syntax perceptually. They move the effort used in the working memory by externalisation and backing “perceptual chunking” of object aggregation (Ullmer et al., 2005, p. 88).

 Human manipulation. Physical constraints support users with a more vigorous feeling of kinaesthetic response, arisen from the passive haptic response offered by token and constraint combo. Furthermore, constraints provide the manipulation of collections of different and diverse physical objects. This can be fulfilled via interaction with complete constraint construction; for instance, moving a rack(s) of tokens as well as via operation such as manipulating within a set of tokens which are constrained together.

An abacus is an excellent example of such a token and constraint system. An abacus represents information as individual beads, as well as the spatial formation and arrangement of the individual components (tokens) within the physical constraint of the counting board and bars. Through the years due to the practical needs of; mobility, managing and maintaining many physical components forced the abacus to develop into a system with “captive” beads (tokens), nevertheless tokens stand to be movable and spatially reconfigurable (Ullmer et al., 2005, p. 90).

Concepts from HCI Ullmer studied a number of essential perspectives and concepts from Human-Computer-Interaction related to understanding and analysing tangible interfaces (Ullmer, 2002). Mostly Shneiderman’s expression of direct manipulation is the more applicable method to the token and constraint

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(Shneiderman, 1983). While initially studied in the framework of visual interfaces, the direct manipulation idea is similarly relevant to tangible systems and interfaces, potentially more applicable compared to visual user interfaces, GUIs. Shneiderman’s direct manipulation theory defines interfaces that support (Shneiderman, 1983):

 Constant presentation of the physical object(s) of concern

 Physical simple actions rather than complicated syntax

 Quick and undo-able interactions which the effect on the object of concern is instantly observable

The first concept, constant representation of the object or objects of concern, ties tightly with the constant nature of tangible user interfaces. The second concept has a distinct link with the token and constraint approach. Constraints act as a manifestation of syntax and convert physical interaction(s) inside their boundary (the constrained configuration and manipulation of tokens) into the implementation of the tasks. The third concept, constraints can also support rapid and undo-able tasks, and immediate visibility (Ullmer et al., 2005).

Some of the Different Kinds of Tokens Holmquist has a famous model for tangible interfaces, in which he proposed the phrases “containers”, “tools”, and “tokens” to categorise physical objects based on a role they have in a system (Holmquist et al., 1999, p. 236). Although this classification remains very important, the token phrase can be used with its original definition, which is more consistent within the domain of computer science and HCI. In other words, for Holmquist tokens can be considered as iconic tokens with the fixed and permanent attachments, containers can appear as symbolic tokens with dynamic and non-permanent attachments, and tools are tokens that are attached to tasks or operations (Ullmer et al., 2005).

It is beneficial to acknowledge Holmquist’s vocabulary in the framework of token and constraint systems. Often tokens are deployed as containers. However, some systems use iconic types of physical representation with permanent attachments, in this manner acting as tokens according to Holmquist. In all

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likelihood, future systems will carry on promoting tokens acting in a range of roles. Holmquist’s classification is beneficial for recognising some of the critical and practical roles that tokens serve in TUI. Concerning the double use of the ‘tokens’ phrase, Ishii and Ullmer’s term phicons is probably a more suitable alternative to address iconic tokens; the tokens with permanent attachments (Ishii & Ullmer, 1997).

Holmquist considered Ullmer’s classification of phicons as symbolically, dynamically attached objects as one rationale for an alternative phrase (Ullmer et al., 1998). In revision, phicon is limited to the iconic, statically attached tokens.

Holmquist’s phrasing seems less suitable to describe constraints. In Holmquist’s language constraints could be thought of as tools, in the sense that they commonly represent operations. Nevertheless, constraints also frame syntax and form workspaces that are not explicitly described by the ‘tool’ phrase. Also, Holmquist suggested the phrase ‘faucets’ for spaces where tokens can be associated. However, for this project, it is essential to identify the constraint phrase from the perspective of a token and constraint system. I have synthesised terminologies with the descriptions used in this project compared to Holmquist’s and Ullmer’s classifications (Table 3).

Table 3. The terminology used in this project;

Compared to Holmquist et al. and Ulmer et al. classifications

Holmquist Ullmer This project Description

Physical objects with Containers Tokens dynamic attachments

Physical objects with Phicons (Physical Tokens Phicons permanent Icons) attachments

Physical structures that are confining Tools / Faucets Constraints how tokens can be manipulated onto an interpretation

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Researchers including Cohen, Ishii and Hornecker argued that “support for group communications” occurs to be one of the most apparent and “compelling virtues” for tangible interfaces (Cohen et al., 1999; Hornecker, 2002; Ishii et al., 2002; Ullmer et al., 2005 p. 96). Ullmer built on Hornecker’s work to highlight the critical factors and effects relating to cooperative uses of a token and constraint system based on tangible interfaces (Table 4).

Table 4. Factors and Effects summarised by Ullmer for Cooperative Use of TUIs;

Adapted from Hornecker’s work (Hornecker, 2002; Ullmer et al., 2005, p. 96).

Facets with special ties to the token and constraint approach are shown in underlined text.

From Table 4 Enabling Factors Positive Effects

#6 Non-Fragmented Constant Visibility Externalisation (Active Visibility Participation) Bodily Shared Space #1 Interpersonal Intuitive Use (Gestural Haptic Direct Interaction Communication) Manipulation #8 Direct Manipulation Awareness (Provide Focus) Parallel Access #3 Shared Access Performative Meaning of Actions

Significantly the factors and effects summarised by Ullmer are directly related to the characteristics which it will be identified later in this chapter and summarised in (Table 7).

The token and constraint approach can have particular implications for several of the characteristics identified in Table 4. Token and constraint interfaces and systems usually deploy physical constraints as the manifestation of tasks. Also, the passive haptic response, physical characteristic, and other features of constraints can arguably have positive effects on collaboration and group interactions. In Hornecker’s vocabulary, the persistent visibility and direct haptic manipulation corresponded with tokens and constraints support concepts such as externalisation, “intuitive use”, “awareness”, and the “performative meaning of actions” (Hornecker, 2002; Ullmer et al., 2005, p. 96).

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Perspectives on Collaboration from Tangible Interaction and Interface Design The physical and collaborative qualities of physicalisations are the primary concern of this study. From the existing research in the field of HCI and in particular tangible interaction and interface design, certain qualities have been identified as essential to support collaboration. Qualities to support; communicating data and being more memorable (Gwilt et al., 2012). Promoting understanding and learning (Bara et al., 2004). Enhancing cognition through the manipulation of physical objects (Anderson, 2003). Analytical tasks (Amar et al., 2005). And increasing both usability and enjoyment via physical interaction (Hornecker, 2011).

In this section, I will focus more on the field of tangible interaction and interface design to identify concepts which can be applied to support collaborative physicalisation. An effective visualisation bridges the gap between the abstract data in its original form and understanding as a result of the visualisation process. In data physicalisation, the meaning-making process is as essential as the question of how people can perceive and interact with data and information. To address these concerns requires a robust and innovative approach. As established earlier, data physicalisation has the potential to follow Kosara’s hybrid method of visualisation, but data physicalisation can also be considered as a growing research area to support sensemaking and communication (Huron et al., 2017).

Moreover, physicalisation is a logical and practical process to empower people to think actively about data-related analytical tasks (Huron et al., 2014). Physicalisation can also support more collaborative data visualisations and explorations. Following an approach similar to System Guidelines for Collaborative Work on a Tabletop display, where users can interact with data and each other simultaneously, supports the better perception of data and more intuitive understanding (Scott et al., 2003). Ullmer and Ishii (2000) argued that interactive systems with physical artefacts as interfaces support collaboration among multiple users and convey a clearer understanding of abstract concepts. Physical interaction supports awareness at three different levels (Price & Rogers, 2004). Firstly, an awareness of the objects being physically handled, and their

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functionality assists users with concentrating on the task at hand. Secondly, awareness at a multi-perceptual level engages multiple senses and provides richer information for reflection in the actual physical world. Thirdly, it helps to concentrate on the contextual information considering given time and location (Price & Rogers, 2004, p. 148). The experience of physical interaction involves senses beyond vision. It provides more in-depth and richer perceptual information from experiences in the environment and/or findings. Physical space supports a vibrant and realistic level of exploration and discovery. This can provide diverse combinations of action and interaction and promote unique and creative exploration. Physical interaction can support various forms of collaboration; physical interactions are part of a continuous learning process. More collaboration requires more communication among users, supporting a higher level of engagement and swapping ideas (Huron et al., 2017).

This project is concerned with collaborative data physicalisation, which is at the intersection of various fields of research, including tangible and embodied interaction design, data physicalisation from data and information visualisation and collaborative tangible interface design (Figure 11).

Data Physicalisation

Collaborative Tangible Tangible Interaction Interface

Figure 11. Collaborative data physicalisation at the intersection of Tangible Interaction, Data Physicalisation and Collaborative Tangible Interface Design

Since the domain of data physicalisation is relatively young and there are limited projects to consider the rich, fluid interaction that exists during collaboration involving traditional, physical and tangible media, I draw upon collaborative concepts from collaborative work on tabletop displays and tangible interactions.

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Over years of experiencing collaborations, users have shaped their skills for interacting with one another and the physical artefacts. Designers must consider these skills and support users’ experience with traditional media (Scott et al., 2003). Scott and his colleagues argued that to support collaborative activity, designs must have an appropriate ergonomic form-factor as well as support the naturalness of the interaction among users. As a result, they suggested eight design guidelines to support collaboration and communication for designing co- located collaborations around tabletop displays, as shown in (Table 5).

Table 5. Suggested design guidelines to support collaboration and communication;

for designing co-located collaborations around tabletop displays by Scott, Grant, and Mandryk (Scott et al., 2003, p. 159)

System Guidelines for Co-located, Collaborative Work on a Tabletop Display

1 Natural interpersonal interaction

2 Transitions between activities

3 The transition between personal and group work

4 Transitions between tabletop collaboration and external work

5 The use of physical objects

6 Accessing shared physical and digital objects

7 Flexible user arrangements

8 Simultaneous user interactions

Next, I will look at the framework of physical space and social interaction from a Tangible Interaction study by Hornecker and Buur (2006). Tangible Interaction (TI) and Tangible User Interfaces (TUIs) are obtaining more and more currency within HCI (Hornecker & Buur, 2006). A big part of the emphasis in TUIs and TI focuses on tangibility and full-body interaction based on user experience with the real-physical-world and provides computational resources and data in

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the material form. Tangible interfaces tend to embed interaction in an everyday setting and support intuitive use. Also, users act within the interface itself, and there are more opportunities for in-person social interaction and stronger collaborations (Hornecker & Buur, 2006; Price & Rogers, 2004; Ullmer & Ishii, 2000).

“The support of social interaction and collaboration might be the most important and domain-independent feature of tangible interaction” (Hornecker & Buur, 2006, p. 439). This quality can be referred to as ‘Social Affordances’ or ‘Collaborative Affordances’ (Hornecker & Buur, 2006).

A broader perspective on Tangible Interaction is aligned with an “expressive-movement-centred” (Hornecker & Buur, 2006, p. 438) view of product design and impact from art. This perspective is concerned with aspects beyond the shape and visual appearance of a product. It emphasises on the design of the interaction itself in a way to benefit from the “sensory richness and action potential of physical objects” (Djajadiningrat et al., 2002, p. 285), so “that meaning is created in the interaction” (Djajadiningrat et al., 2002, p. 288). To design tangible interactions to support social interaction and collaboration, Hornecker and Buur (2006) introduce their framework structure based on 14 concepts which are in turn divided into four themes (Table 6).

Table 6. Themes and Concepts on the Tangible Interaction Framework;

By Hornecker and Buur (2006, p. 440).

Tangible Interaction Framework with themes and concepts

Tangible Manipulation Spatial Interaction Embodied Facilitation Expressive Representation

Haptic Direct Inhabited Space Embodied Constraints Representational Manipulation significance Configurable Materials Multiple Access Points Lightweight Interaction Externalization Non-fragmented Tailored Representation Isomorph Effects Visibility Perceived Coupling

Full-Body Interaction

Performative Action

Considering collaborative data physicalisation is at the intersection of Tangible Interaction, Data Physicalisation and Collaborative System

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Design(Figure 11), I have identified a set of requirements and characteristics for designing a collaborative data physicalisation based on a Minimal set of requirements for any visualisation (Table 2). Suggested design guidelines to support collaboration and communication for designing co-located collaborations around tabletop displays (Table 5) and Themes and Concepts on the Tangible Interaction Framework (Table 6). As presented above, I also adopted Kosara’s minimal set of requirements. I have summarised the characteristics in a set of ten (Table 7).

Table 7. Identified characteristics for designing a collaborative physicalisation

Characteristics for Collaborative Data Physicalisation

1 Interpersonal Interaction

2 Fluid Transition

3 Shared Access

4 Access Point

5 Flexible User Arrangement

6 Non-Fragmented Visibility

7 Simultaneous Actions

8 Direct Manipulation

9 Simple Interaction

10 Externalisation

Some of these characteristics are a concern for user interactions with physicalisations and others involve interacting with the material object of the physicalisation. I will explain each of these characteristics in more detail next.

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Interpersonal Interaction To promote group activities requires considering the interpersonal interaction at the core of collaboration (Scott et al., 2003). This can provide the primary mechanism that people use to interact collaboratively. Interpersonal interactions are based on our everyday ways of relating to one another and the physical world. Hindering these interactions can create interruptions in collaboration. Researchers also highlighted the collaborative significance of interpersonal interactions such as gesturing, deictic referencing, and meeting coordination activities (Scott et al., 2003). This suggests that in order to support collaborative data physicalisation, an interface should support interpersonal interaction.

Fluid Transition It is essential to allow users to move smoothly and efficiently between activities. These activities include interacting with the system, personal and group tasks, and occasionally independent work beyond the interface (Elwart- Keys et al., 1990). Fluid transition works hand in hand with interpersonal interaction and simple interaction. Past researchers indicated that users could adapt smoothly between the group and individual tasks when collaborating. Nevertheless, it is crucial for collaborative systems to support fluid transition beyond the interface (Scott et al., 2003).

Shared Access Pointing or gesturing to a shared object facilitates group communication by providing a clear spatial connection to the object for the gesturer and also for other group members (Bekker et al., 1995). Furthermore, interacting with a shared object can support awareness and group-focus. This is due to the body posture and gaze direction of the group members and their attention towards the same shared object, which can be readily understood by other group members. Based on the nature of the collaboration group, members may interact with a single shared object or with a series of shared associated objects. This suggests that to provide shared access to support collaborative data physicalisation all participants should be able to see what is going on as the action happens and be able to get their hand on the shared object(s) (Hornecker & Buur, 2006; Scott et

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al., 2003). Shared access is closely related to the characteristics of flexible user arrangement, non-fragmented visibility, simultaneous actions and access points.

Access Point Tangible interfaces have a natural spatial property, which means that participants using these interfaces are required to move and interact in the real- physical-world (Hornecker, 2005). These can inform the boundaries of actions and behaviours within the system. These structures can also form the social configuration around the system. Tangible interaction embodies structure; thus with interaction design, we can shape physical structures to support and direct group processes. The notion of embodied constraints indicates the arrangement of the physical system (Ullmer et al., 2005). Embodied constraints such as shape, position or size of objects directly inform the activities and interactions. To access and interact with these objects is at the core of the access points concept. Multiple access points support simultaneous actions and shared access (Hornecker & Buur, 2006). The multiple access points approach is also aligned with the democratisation concept in a way that lowers the threshold and distributes the control, it provides an equal chance for all participants to interact with the system. The primary concern in access points focuses on embodied constraints. This suggests that in order to support collaborative data physicalisation, the physical configuration should assist users to collaborate simultaneously and all the users should be able to get their hands on the objects of concern.

Flexible User Arrangement There are different ways for participants to be positioned about the activity. Whether people sit or stand, the user arrangement can influence interpersonal interactions as well as group potential. People often seem more comfortable to interact at “arm’s length”, since this demarcates their personal space, although age and culture can influence participants’ preferred position (Hall, 1966; Scott et al., 2003a, p. 170). Also, the task required by the activity can influence participants’ positioning. As a result, user arrangement directly impacts how users interact with the system and/or artefacts as well as each other (Scott et al., 2003). This suggests that in order to support collaborative data physicalisation, an interface should support flexible user arrangement.

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Non-Fragmented Visibility Visibility or non-fragmented visibility is one of the natural properties of spatiality characteristics that physical interfaces offer. Physical interfaces exist in the real-physical-world, they take up real space, and as a result, they render non- fragmented visibility. This supports participants pointing and collaborators following their gesture flawlessly to create a condition where seeing is understood as being seen. Interacting in the physical world is observable and comprehensible, which promotes communicative and performative tasks (Hornecker & Buur, 2006). Non-fragmented visibility is linked directly with shared access and supports interpersonal activities, simultaneous actions and direct manipulation. This suggests that in order to support collaborative data physicalisation, non-fragmented visibility should be supported to enable participants to see what is happening and be able to follow the visual references.

Simultaneous Actions When participants work together on collaborative tasks, it is common for them to interact with designed objects at the same time, although participants’ action can be interrupted during collaboration by the limitations and flexibility of the physical system and their interfaces. Teamwork usually consists of different collaborative styles. Some of the common styles include working in parallel, working sequentially in tightly coupled activities, working independently, and working under assumed roles (Scott et al., 2003, p. 171). Participants often adapt to take turns on systems that do not support simultaneous actions, but this can shift the focus from teamwork and collaborative interaction to personal tasks (Shen et al., 2002). It also jeopardises interpersonal interaction, fluid transition, shared access and general awareness during collaboration. This suggests that in order to support collaborative data physicalisation, an interface should support users’ simultaneous action to assist them to concentrate on the task at hand and give them the benefit of using various collaborative styles as required.

Direct Manipulation Tangible interactions offer physical manipulation with physical objects; this means directly manipulating the objects of interests. Physical objects provide tactile feedback, haptic contact and physical characteristics; they attract users’

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sense of touch and offer playfulness and tactile joy. Directness in direct manipulation can refer to the link between action and effect, which can be beneficial when the data is physically presented and handled. It can improve awareness and collaboration (Hornecker & Buur, 2006; Shneiderman, 1983). Direct manipulation is an inherent quality of tangible interaction and effect all of the other characteristics and concepts identified for collaboration. As a result, to support collaborative data physicalisation, an interface should support users to grab, feel and interact with physical objects.

Simple Interaction Simple interactions work hand in hand with direct manipulation, and they are critical for collaboration. Simple interactions constitute small steps that allow users to carry on, share and examine their ideas instantly (Eden et al., 2002). They create conversational interaction, constant feedback, and support group-focus and shared vision. It is noteworthy to mention that while studies aim to make full use of the tangible objects and their beneficial relation between cause and effect, researchers often suggest maintaining simple interaction and direct mapping, as complicated interaction and mapping can affect the readability and comprehensibility of the system (Hornecker & Buur, 2006). Thus, to support collaborative data physicalisation, an interface should support users to proceed in small, exploratory steps with instant feedback while interacting.

Externalisation Tangible interaction is a concern with the physical representation of data. As humans, we generate and share externalisations of our thoughts to assist our cognition by supplying shared references directly or indirectly. Cognitive psychologists use the term ‘external representation’ as a result of the interpretation of occurrences in the external physical world; these interpretations consist of spatial connections and can be differentiated from the internal cognitive model (Zhang & Norman, 1994). Externalisation can facilitate creating a meaningful mental image. Ullmer and Ishii used the phrase ‘representational significance’ to refer to physical tokens that manifest fundamental characteristics of the system and how comprehensible they were for users (Hornecker & Buur, 2006; Ullmer & Ishii, 2000, p. 917).

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The notion of externalisation refers to the interrelation of physical objects’ representations and how users comprehend them. Also relates to the concept of tacit knowledge (Glaser & Resnick, 1989), which is learned through informal learning and involves personal experience (Wagner, 1991). The kind of knowledge that people acquire and empowers them to conduct a task, which is “tacit” and “implicit” and is not easily “expressed” or “verbalised” (Jong & Ferguson-Hessler, 1996, p. 109).

Thus, to support collaborative data physicalisation, an interface should support users to think and interact via or with the physical objects. To use the objects to support their act or interaction and support the group focus and conversation. This can also be beneficial when a design aims to support communication, negotiation and shared understanding.

The Gap in the Literature for Data Physicalisation Based on the examples of data physicalisations presented earlier in this chapter and in particular their characteristics that support collaboration and visualisation, it is notable that few of them explicitly consider data physicalisation as a collaborative means for visualisation. However, some examples are better candidates for collaborative physicalisation than others. For instance, From Over Here, Physical Bar Chart and MakerVis. In the case of Physical Bar Chart, it is easier to identify the potential for collaboration since the project has a participatory approach. I would also like to highlight most of the static examples support the essential characteristics of shared access, access points, simultaneous actions, direct manipulation and interpersonal interactions. These characteristics, in particular, are missing from the dynamic data sculpture examples. In most cases, users will interact with these sculptures through digital applications, and therefore direct manipulation is missing. By using digital applications and interfaces to interact with data sculptures, support for simultaneous actions, shared access, access points and interpersonal interactions have been limited or neglected.

Furthermore, some data physicalisations remained merely extruded 2D visualisations, for example, a 3D-printed line graph (Brown & Hurst, 2012). However, more is possible. As Hornecker describes “The term ‘tangibility’ refers

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to the peculiar double-sided characteristic of the sense of touch; that one cannot touch something without being touched oneself, being at once active and passive” (Hornecker, 2011, p. 21), which provides tactile feedback, haptic contact and support instant feedback. Moreover, other unique physical characteristics of data physicalisations such as being persistent which means they are always ‘on’ (Gwilt et al., 2012) makes them suitable to support visibility and flexible user arrangement. Physical presentations enable users to choose their preferred perspective and could have a catalytic effect on understanding data. For instance, using tangible physical molecular models was beneficial for both concrete and formal thinkers in a group of chemistry students (Gabel & Sherwood, 1980). According to Gabel and Sherwood, this improvement can be because students paid more attention to the contact while manipulating the model compared to the time observing the teacher manipulating the model for everyone (Gabel & Sherwood, 1980).

2.5 CHAPTER SUMMARY

In this chapter, I have highlighted the importance of data physicalisation and how the emergence of digital fabrication can support the making of data physicalisations. Next, I presented some well-known data physicalisations, from ancient times to today. I highlighted that most of the data physicalisations fall under the artistic approach and can be classified as data sculptures. I went through examples from static and dynamic physicalisations to look at their physical and tangible interaction design and qualities and to understand how people interact with them. I also highlighted some of the common approaches to physicalisations, to make them more meaningful and engaging including the use of personal data-sets, concrete scale and metaphors. Furthermore, based on the presented examples I highlighted the characteristics most suitable for collaborative data physicalisations. I identified a minimal set of requirements for data physicalisation to support data and information visualisation based on Kosaras’ minimal set of requirements for any visualisation.

Even though this project is a physical and non-digital approach, I placed collaborative data physicalisation at the intersection of tangible and embodied interaction (Interaction Design), data physicalisation (Data and Information

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Visualisation) and collaborative systems design (HCI) to apply insights from these fields to the design of the collaborative physicalisation system. Based on this placement, I identified ten critical characteristics required for collaborative data physicalisation. Moreover, I explained each characteristic and how it can support collaborative data physicalisation. Next, I introduced some of the relevant theoretical concepts, including Embodied Interaction and Affordances, Boundary Object and Token and Constraint System and how each can support this project.

Screen-based visualisation remains the prevalent form of most current research on visualisations. This project requires further investigation to apply the learning in the field of designing a collaborative data physicalisation to provide a better understanding of visualisations beyond the screen for everyone in a more collaborative manner.

Data physicalisations can become common as part of everyday activities, with the inherent instant feedback ability to notify when decisions and changes are made, allowing people to investigate cause and effect relationships and providing them with pleasing and contextually-connected experiences to stimulate and inspire them to interact and collaborate (Vande Moere, 2008).

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Chapter 3: Design Research

This chapter describes the design research used in this study to address the main objectives identified earlier in chapter 1. Section 3.1 presents the methodology adopted in this study, and the design research. Section 3.2 introduces the participants. Section 3.3 lists all the tools and technology used in this study and explains their use. Section 3.4 provides a summary of the methods used at the different phases of the study. Section 3.5 outlines how the data was analysed, and section 3.6 presents a summary of ethical considerations and limitations.

3.1 METHODOLOGY AND METHODS

In the context of this study, the relationship between design and research is the most important, followed by how design can be considered part of the research process. This study adopted a qualitative methodology that involved observation and semi-structured interviews with focus groups mainly comprised of rent-paying household residents in Brisbane.

The core of this design research project was the production of various design iterations and reflections, to design and make a collaborative physicalisation system flexible enough to map the chosen data by participants from the design evaluation sessions.

The two different stages of this study included: stage one, design, make and reflect on the physicalisation, and stage two, the design evaluation to test the physicalisation system with participants. Data were collected via audio-visual recordings and field notes, and thematic analysis was used to integrate the collected data.

The project’s research questions established the relationship between the methodological framework and the research approach. According to Blaikie, the most critical components of any research design are the research questions, since answering these questions directs the research activities (Blaikie, 2009). Further, decisions about other features of the design research approach taken here can be

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made based on how they provide a way to address the research questions. In brief, defining the research questions is the starting point for the research design (Figure 12).

Figure 12. Research questions and research through design approach

In this project, relevant methods and techniques were used to collect and analyse data according to the chosen methodological framework; research through design and research questions. The qualitative methods used to collect data were observation and focus groups as well as audio-visual recording, semi- structured interviews, and field notes. Raw data comprised video and audio files, field notes, and transcriptions, which were analysed using thematic analysis approach.

Research questions are fundamental elements of the research design and must be formulated, so they are clear and direct. Research questions can be divided into three main groups: ‘what’, ‘why’ and ‘how’. It is crucial to understand the differences between these questions, since each relates to a different research aim and motive. This research project focuses on ‘what’ and ‘how’ questions. ‘What’ questions seek descriptions, and ‘how’ questions are interested in interventions that generate changes (Blaikie, 2009).

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This project concerns collaborative data physicalisation. RQ1 addresses the ‘what’, and RQ2 and RQ3 address the ‘how’. Thus, the three research questions for this thesis are as follows:

RQ1. What concepts from tangible interaction and information design literature can be applied to support collaborative physicalisation?

RQ2. How can a collaborative physicalisation system be designed and made?

RQ3. How do people employ data physicalisation as part of their collaborative sense-making and meaning-making?

After formulating the research questions, the next step is identifying ways to answer them. This depends on the question type. Questions seeking an answer to a ‘what’ can be answered by making proper observations or measuring some values—for example, to answer a ‘what’ question, relevant qualitative data can be collected and analysed, producing some form of qualitative description based on the result; a similar approach was applied in this project for answering RQ1. However, in any case in which the observer is an active participant in the process, he/she should make many decisions during the process before producing the description. During this process, the observer should be aware of his/her preconceptions.

To answer a ‘how’ question, a different approach is appropriate, as a different kind of description is required. Usually, the answers to ‘how’ questions should describe a state of activities and provide a possible approach concerning how to achieve the intended outcome. ‘How’ questions demand researchers to have a clear understanding of the research area being studied and explored (Blaikie, 2009).

Design Research; Design and Research The core of this design research project was the production of various design iterations and reflections, to design and make a collaborative physicalisation system flexible enough to map the chosen data by participants from the design evaluation sessions.

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Since the research questions in this study touch on both design and research, I adopted a research strategy that engaged with both design and research. To fulfil this requirement, I followed the design research approach. Research strategies are mainly a collection of actions that provide a logical method for answering the research questions, and multiple approaches have been established for constructing a research strategy. According to Blaikie, to select a research strategy or mixture of strategies is the second substantial research design decision. Blaikie identifies four research strategies: inductive, deductive, retroductive and abductive. He believes that social sciences knowledge can be advanced using one or a mixture of these strategies (Blaikie, 2009).

Each research strategy provides a different approach to answering the research question, indicates a different point for starting and concluding, and explores different paths in between. To answer a ‘what’ question, the inductive strategy is vital. For RQ1, this strategy can support to identify the concepts from the field of data and information design as well as tangible interaction and information design that can be applied to support collaborative physicalisation. The inductive strategy starts with collecting research data and then using inductive logic to extract a form of generalisation. This strategy seeks to describe social aspects and the connection or the nature of the regularities in a social setting (Blaikie, 2009). The goal of inductive research is to develop descriptions of aspects, patterns, principles and concepts. In this study, the inductive approach began with collecting data on characteristics, continued by producing descriptions, and concluded by relating findings and results to the research question(s).

There is more than one way to define research, and there are several scholarly approaches as to how research should be carried out. Archer describes the nature of research in terms that are consistent across most of these approaches (Archer, 1995). He considers research to be a systematic enquiry, the goal of which is communicable knowledge. Some tension exists in how research is understood when considered alongside creative and design practice. Sometimes, designers or artists claim that what they do is research and that

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creative practice or design activity is the same as a research activity, because their output, which can take the form of artworks or design products or other creative endeavours, produces new knowledge. This is crucial for this study, since RQ2 in particular focuses on designing and making the collaborative physicalisation system and the report of making.

In attempting to answer this question, it can be useful to distinguish between “research about practice” “research for the purposes of practice” and “research through practice” (Archer, 1995, p. 13).

According to Frayling, the work of an artist or designer may be ambiguous in terms of being classified as research. He refers to Herbert Read’s Education Through Art, and the debate about what it means to consider an artist or designer as a researcher. Frayling highlighted that “the artist [or designer] is not in the business of unambiguous communication” (Frayling, 1994, p. 2). To adapt Read’s famous distinction about art and education (Read, 1943), Frayling categorised research in art and design as follow:

 research into art and design

 research through art and design

 research for art and design Candy provides another way to understand the relationship between practice and research. She recently distinguished between two kinds of practice- related research: practice-based research and practice-led research (Candy & Ferguson, 2014). That is, “if a creative artefact is the basis of the contribution to knowledge then the research is practice based. If the research leads primarily to new understandings about practice, it is practice-led” (Candy, 2006, p. 1,3). (Table 8) summarises and compares Archer’s, Frayling’s and Candy’s classifications: Table 8. Summary of Archer’s, Frayling’s and Candy’s classifications

Bruce Archer Christopher Frayling Linda Candy

Research for the Research for Art & Practice-Based purposes of Practice Design Research through Research through Art Practice-Led Practice & Design

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Design Research and Research Through Design Approach Candy explains practice-led research as an approach that involves practice as an essential component of its approach. This aligns with Frayling’s research through design approach and could be compared with Archer’s research through practice approach (Table 8) (Archer, 1995; Candy, 2006; Frayling, 1994). In design research, it is complicated and dangerous to generalise research findings, since the research approaches are situation specific. The term situation specific is essential here and refers to two crucial aspects: 1. the skills of the designer, and 2. the knowledge gained through design and/or practice. First, it is through the designer’s skills that problems are resolved. Here, the definitions of the problem and solution are not as distinct as they are in conventional research but are more interlinked. Secondly, the knowledge obtained from these kinds of research projects are relevant to a specific situation (Archer, 1995).

Design as Part of the Research Process The design process in this project was reflective and iterative and could be understood as a reflective research method. It involved the reflective aspects of research, resonating with Schön’s descriptions of the term reflection in action and design as a process of listening to the situation talk-back during professional practice (Schön, 1995). That is, the practice involves engaging in a “reflective conversation” with the materials of design (Schön, 1992, p. 3). In this study designing the final collaborative physicalisation system was the outcome of the reflective design process. The entire process of the form making (section 4.2.1), testing the initial system to represent a data set (section 4.2.2), testing the system to represent the rain-data (section 4.2.3), creative exploration sessions (section 4.2.4) and getting familiar with the context of use (section 4.3) constituted the process of reflective research and design and listening to the talk-back that informed the design of the final system (section 4.4).

Archer also emphasises the necessity of engaging design researchers through practice. He considers a multi-layered and complex approach (Archer, 1995). This project similarly pursues and provides context-specific insights. As this research is particularly situation-specific, the design evaluation sessions in this research were situation specific to the energy consumption mapping activity

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of household members in the greater Brisbane area. Situation specificity is a reminder of the importance of context in generating understanding and knowledge because design research is attained by engaging in action directly and as a part of the research process in the real world (Frayling, 1994). The design research is relevant to a specific case and its design process. Archer highlights that researchers should situate themselves in assumptions and theories identifying the area in which the knowledge from their research is relevant. Knowledge can be gathered on two levels (Archer, 1995). In this project, one can research concepts from tangible interaction and information design literature that can be applied to support collaborative physicalisation and how do people employ physicalisation as part of their collaborative sense-making and meaning- making (RQ1&3); however, one also can research the process of making and how can a collaborative physicalisation system be designed and made? (RQ2).

Conducting research through design weaves both levels of knowledge together. In this project, the physical set of artefacts as a collaborative physicalisation system was designed and made to explore how people employ this approach as part of their collaborative sense-making and meaning-making in the context of energy mapping at the household level. Usually, such an approach can serve as inspiration for further studies. Turning these ideas into a robust approach that can be experienced is a significant design contribution. Generally, the research through design approach assumes that knowledge is acquired as the result of some experimentation, and it is possible to generalise this knowledge in the form of design specifications in new schemes or theory. This could be considered the fundamental difference between design practice and research through design. The design research is informed by research questions and leads to transferable knowledge. Also, design skills are essential to do research through design. In short, it would be wrong to match research through design and design practice, as Fallman clearly divides them based on their aims. He considers knowledge the goal of research through design, and an artefact the aim of design practice (Fallman, 2003).

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As stated above, this research is reflective as well as iterative. In this project, an iteration typically included theorising/planning, engaging/creating and reflection/evaluation to establish and refine the research findings (Figure 13).

Figure 13. A cycle of iteration in this research

This project pays particular attention to the design of the artefacts as the components of the collaborative physicalisation system (RQ2) and studies how do people employ data physicalisation as part of their collaborative sense- making and meaning-making (RQ3). Artefacts can become things to think with, supporting people in thinking through problems, by involving people in the process, they are able to provide and contribute to the iterative process. This research can benefit from this concept to answer the ‘how’ question in RQ3. As Brandt argues, the communication provided by design artefacts or prototypes is not one-way and has the potential to provoke reflections. This can allow participants to re-see the design in a way that gives it a new meaning (Brandt, 2007). Through interaction, participants can combine the different precepts and bits of information with time to create a more vibrant visualisation of the data and gather understandings (Card et al., 1999). Interactivity can also have social purposes, for instance assisting people to communicate and coordinate in collaborative environments or assisting users in presenting data in a more meaningful way.

Finally, I report on the design process that can be achieved through a process of reflective design and practice. Studying and evaluating the results from interactions with artefacts in each iteration, as well as referring to personal experience and reflection as a designer, allows me to address RQ2 by providing a set of recommendations that other design researchers can use in their work. In the next section, I explore and expand on my approach involving tangible interaction design.

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Tangible Interaction To integrate the concept of tangible interaction into this design research meant integrating it into the design process. In doing so, the tangible interaction also benefits from the understanding and expertise that are part of the design interactions. Tangible interaction is thus not an add-on to this project, but an integral part of it. This project approaches design as a tool in research through design and uses, explores and validates the knowledge from the field of tangible interaction in a physicalisation context (RQ1). It is beneficial to recognise van den Hoven’s perspective: “that design does not only apply knowledge, it also transforms knowledge” (van den Hoven et al., 2007, p. 112).

This project could benefit from tangible interaction design by including more diverse and challenging design in the context of use rather than using a common approach to design for traditional desktop applications or systems. The project initiated the design process from scratch for physical interactions with data by creating a physicalisation system rather than an extension to existing digital applications, with the aim to lead to an approach that yields compelling prototypes and new knowledge.

To test the final system with end-users to create a collaborative data physicalisation in the context of energy consumption at the household level, I conducted the data physicalisation activity with five different households in the greater Brisbane area. Each session took from 70 to 80 minutes. I spent 10 to 15 minutes setting up, collecting the informed consent forms and delivering a brief explanation of the activity. The activity took around 60 minutes, and the conclusion took between five and 10 minutes.

I started the session by asking the household members to suggest where we should sit and set up the scene for our activity. Out of the five study sessions, three were set up on the dining room table, and two were set up on a coffee table. After setting up the camera to record the activity, I collected the informed consent forms from household members, and I confirmed with others what would be recorded by pointing at and showing the camera’s attached screen and confirming the area to be recorded before I started the recording. The camera was mounted on a tripod, and it was set up to record from a top perspective,

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mostly focused on the physical board and interactions around the board, tokens and tip-cards. There was no camera operator, and the whole session was recorded from the fixed perspective. This was intentional to avoid any interruption during the session and to concentrate on the activity and physicalisation tasks.

3.2 PARTICIPANTS

In this study participants were adults aged 18 and older who lived in five different rental properties in the greater Brisbane area. There was no distinction of gender, political affiliation or nationality. Participants were recruited through the personal-professional network.

Ten participants in total across five households in the greater Brisbane area were sought. For each household, the research involved group interview sessions in the households to understand how participants might use a data physicalisation designed by the researcher in the context of energy consumption in their household.

3.3 INSTRUMENTS; RESEARCH METHODS

The term method as defined here indicates the qualitative researcher’s approach to collecting data to form their argument. All qualitative research methods, regardless of their paradigmatic choice, are conducted in a real setting among real people (physical world). They concentrate on participants’ behaviour in the context of research and make sense of the meanings conveyed by them. They consider different aspects of the social, cultural and physical context of each participant regarding their living, working and interacting. Qualitative research approaches are well known for their dedication to comprehending and learning about others’ perspectives, rather than promoting the researcher’s vision. However, the position, level of involvement and influence of the researcher remain critical in this method. Qualitative approaches also clarify and define diversity among people’s behaviours and assumptions. With a qualitative research approach, these diversities and differences are made understandable by considering the context and situation of the research study. Being able to make

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sense of and adopt the perspective and lived experience of people is at the core of proper qualitative research (Given, 2008).

In qualitative research, some methods, such as observation, are fundamental. Qualitative researchers prefer methods such as interview and audio or visual documentation, depending on factors that include the study context, time available, financial resources and cultural or situational appropriateness.

In the next section, I cover my primary approach towards research methods, data-collection techniques and data-analysis methods, as well as reasons for the choice of methods and their strengths and limitations. (Figure 14) shows research methods at different phases of the study.

Figure 14. Research methods at different phases of the study

Observation Observation involves gathering impressions of a situation by looking and listening in an organised and meaningful manner to comprehend and learn regarding the research concern. Observational research is widespread in both quantitative and qualitative approaches. This study focuses on qualitative observational research. The qualitative observational research aims to capture life as participants experience it through research rather than the researcher’s predetermined classification and or their preconceptions. The observational research considers behaviours to be meaningful and purposeful and to contain

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profound values and understandings. This approach can help to answer how recruited household members within the greater Brisbane area employ data physicalisation as part of their collaborative sense-making and meaning-making in the context of energy consumption at the household level (RQ3), which makes an observational approach crucial. It is common to conduct observations in a natural setting to capture behaviours as they occur in the real world. This typically includes direct contact among researchers and participants through indirect data-collection methods (Given, 2008).

As a result, to answer RQ3 and to capture participants’ behaviours in a natural setting, I visited household members at their residence and used an audio-visual recording technique, an indirect data-collection method, to collect data. Qualitative observational research is exploratory and, in this case, was undertaken to discover unanticipated phenomena that align with the project’s primary objective. Observation uses the kind of reasoning which creates meaning from the observed behaviours being developed during and after data collection. Qualitative observation is constructivist7, and highlights the significance of the meanings that participants bring and acknowledges the researcher’s subjective position and characteristics, as both observed and observer at the same time, which cannot be avoided (Given, 2008).

Qualitative observational research can be conducted on its own or used with other methods. In this project, in addition to observation, I used focus groups, audio-visual recordings, semi-structured interviews and field notes to collect data. I review each of these methods in more detail in the following sections.

I start by describing some aspects of qualitative observation and the connection between this method and my research topic and research questions, highlighting the concerns about the researcher’s position as an observer,

7 As Given describes “Qualitative observational research […] is constructivist in approach, emphasizing meanings that the participants attach to activities and events. [It] recognizes the subjective role of the researcher. It acknowledges reactivity to be inevitable on the part of both the observed and the observer and seeks to address and understand this through researcher reflexivity.” (Given, 2008, p. 573)

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discussing some of the ethical issues, and noting the strengths and weaknesses of this approach.

Similar to many other qualitative research approaches, observation starts with a research concern or research problem. This usually illuminates part of the research interest and adds more detailed research questions. In this project, the research concern and research questions focus on the collaborative physicalisation system, the relevant concepts from tangible interaction and information design to support collaborative physicalisation (RQ1), the process of designing and making such systems (RQ2), and how do people employ data physicalisation as part of their collaborative sense-making and meaning-making (RQ3).

Some researchers choose to start observation without having pre- conception to avoid developing any predetermined ideas and assumptions; however, in this project, like many others, I conducted a literature review to identify the gaps in the field and also to familiarise myself with some of the concepts in the field to develop an answer for (RQ1).

Qualitative observation requires an iterative approach, moving back and forth throughout the research process. Researchers collect data through observation, which they articulate over iterations of analysis. Themes and patterns identified during each iteration inform the area on which to focus for the following observation. I provide more detail on these iterations in the data analysis section.

The researcher’s position as an observer is critical with regards to what can be observed. This depends on the extent to which the researcher becomes involved in the observation setting. The researcher could remain just an observer, with no interaction between researcher and participant, or could become a participant, or operate anywhere in between (Figure 15).

Observer Researcher Position Participant

Figure 15. Researcher position in the observation

Adler and Adler argue that all observers participate in a setting in some way, and use the phrase membership, from minor to full, to explain the researcher’s

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position (Adler & Adler, 1987). Several key elements indicate the researcher’s position as an observer, participant or anywhere in between, including the aim of the observation, the researcher’s characteristics, and the nature of the setting. In an observational study, it is critical to define the researcher’s position and his/her involvement when presenting the findings. In my research, researchers acted as facilitators and tried to remain more observers than participants both during the creative exploration and design evaluation sessions.

Various ethical concerns must be addressed for observation. The most important relates to the researcher’s position as an observer and the possibility of misleading the research, either by choosing the incorrect role as researcher and observer or misleading the participants about the research. Other ethical concerns include seeking informed consent, preserving confidentiality, protecting the participants’ identities and being mindful of sensitive information collected during observation. In this project, all participants were de-identified with a simple lettering system and all data, including audio-visual recorded files, was stored in password-protected folders on a computer/server. The ethical researcher must be inclusive during observation and truthfully represent the voices of all participants. Dominant and shy or less powerful participants should be treated equally, and the researcher must ensure that all participants have a fair chance at participating and having their say. This ethical concern was particularly important for this study, as one of the influential concepts for this study was democratisation through physicalisation. In addition to addressing these concerns during my observation for this study, there was no distinction made for participants’ gender, political affiliation or nationality. Sessions were carried out in an informal fashion and questions were not personal. Also, participants were informed about what would be recorded during the session before they agreed to participate in the research. It was also made clear to participants that they could withdraw at any time without any consequences. In general, to minimise the risks identified above, I assured participants that they did not have to answer any questions or take part in any activities with which they were not comfortable. I facilitated the discussion to direct it where needed and, from time to time, confirmed and clarified with participants what their responses meant so that everyone had a clear understanding of what was taking

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place. This is critical, as observation is usually used to explore various behaviours and aims to be as inclusive as possible.

Qualitative observation, with its characteristics and design research approach, is a powerful tool for exploratory topics about which little is known, such as the collaborative physicalisation. Observation is well suited to learning about and analysing behaviours, and findings can result in a richer and more complex understanding of phenomena. It has the flexibility to be combined with other methods such as interviews, audio-visual recordings and others. It is also suitable for discovering new information, verifying existing knowledge, theory generation and theory confirmation.

Observation also has some limitations. Like many other methods, observation is not appropriate for all research approaches, as certain aspects of research cannot be directly observed. Sometimes, lack of repetition makes it challenging to observe and capture particular acts that occur. Observation is time-consuming and situation specific. It is difficult to transfer findings from one setting to another and repeat observations, as this usually requires detailed descriptions of what has been observed to help readers fully understand how the findings are transferable to other settings. Observation strongly relies on the researcher’s position and performance as an observer and is subject to the researcher’s understanding and analysis. Some strategies to address these shortcomings include becoming acquainted with the field and being prepared for the unexpected, checking and establishing the validity of data sources and methods triangulation, member checking, peer reviewing debriefing, and maintaining detailed descriptions of the observation. To address these issues in this project, I considered additional methods in conjunction with observation, including audio-visual records, semi-structured interviews and field notes, which I will discuss in this chapter. It is worth mentioning that with observation, there will always be multiple accounts of what is taking place, and the researcher may shed light on only one or a few accounts, depending on the research objectives and the researcher’s role as an observer.

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Design Evaluation; Focus Group and Group Interview Focus group is a qualitative interviewing method that uses a researcher-led group discussion to generate data. Since the mid-1980s, the focus group method has become prominent among social science researchers because of its flexibility, similar to how an individual interview can be modified across a broad range of approaches to address an extensive variety of intentions (Given, 2008; Stewart & Shamdasani, 2014). As a result, it can be used for exploratory research, where participants debate a topic freely as seems appropriate, or it can be conducted in a more structured manner, where the interviewer or researcher takes an active role in controlling the concerns to be debated. These discussions can be adopted as a type of data collection, which is the fundamental aspect of the focus group method. It is notable here there is no need to come to an agreement or fixed conclusion during the discussion; instead, the participants’ conversation itself is the matter of interest. This method was appropriate for this project because of its exploratory nature. During each design evaluation session with the household members, their interaction with the system and the conversation was the concern of the study, and the audio-visual recordings were the primary source of collected data. The project interest was on how participants employed physicalisation as part of their collaborative sense-making and meaning-making. Participants were free to discuss their experiences during the session and their energy mapping activity. These conversations and interactions were the focus of this study.

As different forms of group interview are present in social science; the term group interview often refers to focus groups for almost all forms of group-based data collection. Although the history of the focus group goes further back in time—since the early 80’s—two of the most popular places where focus groups have been used are evaluation research and survey research. Using a focus group as an exploratory method to survey became well established in marketing research and, over time, become popular in academic research, particularly for projects with topics about which little was known. For evaluation research, focus groups are used in different stages, including initial and follow-up stages. By gathering people who share similar backgrounds and common interests, a focus group provides a chance for the researcher to hear participants’ experiences and opinions in a meaningful conversation about the project. A focus group can

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provide an opportunity for a meaningful conversation that improves the researcher’s understanding of a topic (Given, 2008). This study gained from the focus group approach at one level during the design development and reflective process stage. On a different occasion, I organised informal creative exploration and design evaluation sessions with fellow researchers. And later, at a different level during the design evaluation stage with household members.

3.4 PROCEDURE AND METHODS

This section provides a summary of the methods used at the different phases of the study (Figure 14).

Data-Collection Methods In qualitative research, the word data is mostly connected with words- based information. Therefore, data analysis assesses participants’ words and other empirical evidence, including audio and visual data (Given, 2008). Qualitative researchers use many methods to collect data. In this project in addition to my main approaches of observation and focus groups, I used other methods to collect data, including:

 audio-visual recording

 semi-structured interview

 field notes

Audio-Visual Audio-visual recording provides a more transparent and powerful technique for collecting data; audio recording captures richly detailed aspects of tonality and emphasis, and video recording captures non-verbal communications, interactions and behaviours (Jordan & Henderson, 1995). This feature was necessary for this study to answer the how question in RQ3: How do people employ data physicalisation as part of their collaborative sense-making and meaning-making, as this level of richness and detail was required. Also, recording and editing audio-visual data is relatively easy and economical, providing a stable source of collected data that can be reviewed many times. The ability to review the collected data was essential during the analysis stage as it clarified what really taking place during the activity. However, as with many

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other research methods, there are some ethical, analytical and practical concerns for researchers using audio-visual recording. I address some of these concerns later in this chapter in the ethics and limitations section.

Semi-Structured Interview The semi-structured interview is a type of qualitative data-collection method in which the researcher asks participants or stakeholders sets of fixed but open-ended questions. An open-ended question is the kind of question that a researcher raises with participants to understand how they became involved with the research topic. Therefore, it provides participants with the freedom to become involved with self-selected aspects of the research topic that are meaningful to them. By adopting this method, I could explore the situation from participants’ perspectives, to find out more about their opinions and allow new ideas to emerge in conversation (Given, 2008). Semi-structured interviews involve no predetermined sets of answers, which allows for new ideas. The semi- structured interview questions were derived from the research questions and enabled me to clarify the gathered input data, obtain useful information in this study and in particular to finalise the answer for RQ3 and my overall understanding.

Field Notes Field notes are among the most typical and natural data-collection methods in qualitative research; they are a kind of personal journal exclusive to each researcher. Field notes are usually written in a first-person, freestyle format. Fetterman divides field notes into two groups: observations and personal reflections (Fetterman, 1998). In this project, like many others, field notes were an additional tool for collecting data rather than the primary data collection method. They recorded descriptive elements of details that could not be captured through other means, such as certain attitudes and gestures or off-the-record comments, details about the setting, temperature, smell, mood and backstory as well as researcher impressions, feelings and hypotheses. It is vital to write field notes soon after each activity in as much detail as possible (Given, 2008). In this project, I wrote my field notes almost immediately after each participatory session, and included details about the setting, such as where participants choose

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to set the system, whether it was on a dining table or a coffee table. Sometimes, during the sessions, I took notes to make sure I would come back to them later or cross-reference them with data collected through audio-visual recording and or another researchers’ observation. Because of the audio-visual recording, my notes were less comprehensive in text and mainly acted as a reminder for me to check a particular concern, action or interaction from the audio-visual recording.

3.5 ANALYSIS

Analysis and observation through audio-visual recording is an empirical method of researching human interactions with other people, objects and artefacts. This method has its origin in ethnography and is known for its essential record-and-playback feature for analysis. It has the vital quality to replay sequences of interaction repeatedly for more than one observer on different occasions (Jordan & Henderson, 1995).

This method enables researchers to discover critical data on the details of social interactions in time and space and, especially in natural settings, the interactions between participants (Lave, 1991; Wenger, 1998). This method raises questions, such as how people make sense of each other’s responses—by playing the video repeatedly, I resolved some questions I had about what was taking place in the video in contrast to what I thought I saw and had initially written in my field notes.

This project’s primary focus is on a collaborative physicalisation system and how people employ physicalisation as part of their collaborative sense-making and meaning-making, which means gathering and analysing an intense volume of behavioural attributes. In addition, since there is no other expressive language defined for bodily behaviour (Jordan & Henderson, 1995), the audio-visual method was appropriate for collecting and analysing data.

Thematic Analysis Thematic analysis is a technique for finding, studying and outlining patterns across data. Transparency and accuracy in the data-analysis process are crucial to evaluating any qualitative research study. However, there is no single correct answer or approach to achieve this clarity. Thematic analysis contrasts with

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other analytic methods that explain patterns within qualitative data. The general aim of other analytical methods is to produce a reasonable and logical theory, but thematic analysis usually investigates within an entire data set to identify a theme. A theme in thematic analysis represents something substantial and meaningful about the data that is connected to the research question. A theme might emerge often or rarely across the data set; therefore, researcher’s verdict is key to identifying a theme. Expressed another way, a theme’s importance is not based on how often it appears, but on how substantial it is in regard to the research questions (Braun & Clarke, 2006).

With thematic analysis, themes and patterns can be inductive or deductive. An inductive or bottom-up approach means the patterns recognised are strongly tied to the data (Patton & Patton, 1990); this level of analysis requires the researcher to investigate deeper into and within the collected data. The inductive analysis is a procedure of coding without considering a pre-existing coding frame; this type of thematic analysis is data-driven. By contrast, a deductive, top-down or theoretical approach leans strongly towards the researcher’s theoretical and analytic concerns and is mostly analyst driven (Boyatzis, 1998). This type of thematic analysis is more likely to focus on analysing an attribute of the data and offers less description of the data overall. In summary, one can code for a particular research question (deductive), and/or a specific research question can transform along the coding process (inductive). In this study I engaged in both an inductive and deductive approach. The inductive approach started with identifying the observed interactions from the audio-visual recordings and grouping the themes in a bottom-up process; the deductive approach was where I looked at identified characteristics from a literature review in a top-down process to match with the identified themes.

Semantic or Latent Another aspect of the chosen themes when using thematic analysis is whether these themes are at the semantic and explicit level or the latent and interpretative level (Boyatzis, 1998). With semantic, themes are selected within the specific, more limited and straightforward level of understanding of the data; in this case, the researcher or analyst does not search for a richer understanding

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deeper and beyond what has been literally said or written during the data collection session. By contrast, latent analysis means the researcher goes deeper into and further than the semantic content of the data and searches for underlying concepts. Thus, identifying themes with a latent approach requires both analytical and interpretive work; the outcome of the analysis is not just a description and contains some level of theorising. To answer RQ3 in this project requires latent analysis in order to understand collaborative sense-making and meaning-making at a more meaningful and profound level.

Thematic analysis can be considered from a realist or constructionist approach. This study uses the constructionist view, treating meanings and experiences as socially created rather than inherently present within individuals. As a result, the thematic analysis does not pursue a target and is not inspired by individual psychologies; instead, the data are explored to speculate on the sociocultural context of the design evaluation sessions in this project: the households with participants. The thematic analysis that targets latent themes is more likely to be constructionist (Braun & Clarke, 2006).

Conducting Thematic Analysis Thematic analysis begins when the researcher or analyst starts to recognise or search for patterns or interesting possibilities in the data. This could happen during an early data-analysis stage or even earlier, during the data-collection phase. To conclude is to report on recognised patterns and themes and their meaning in the data set. Braun and Clarke provide a guide that includes six phases of analysis (Braun & Clarke, 2006, p. 87), (Table 9).

It is critical to highlight that thematic analysis is not a linear process that easily moves from one stage to the next, but requires moving back and forth through phases as required — thus, it is a time-consuming process and should not be rushed (Braun & Clarke, 2006). Becoming familiar with collected data requires repeated readings of it and reading in an active way: looking for meanings, patterns or any interesting concerns.

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Table 9. Phases of thematic analysis by Braun & Clarke

Six Phases of Thematic Analysis by Braun and Clarke

1. Familiarising yourself with your data

2. Generating initial codes

3. Searching for themes

4. Reviewing themes

5. Defining and naming themes

6. Producing the report

When generating the initial code, coding partially relies on if themes are more “data-driven” or “theory-driven” (Braun & Clarke, 2006, p. 88). Data-driven themes will depend on data, but theory-driven themes usually seek to answer particular questions that the researcher or analyst tends to code around. In the next section, I will explain each phase for analysis in this project based on the guideline provided by Braun and Clarke.

1. Becoming Familiar with collected data: At this stage, researchers transcribe their data if necessary. This stage is all about getting to know the collected data better; reading and rereading the data and making notes on preliminary ideas. In my study, I used the audio-visual recorded files. I separated the audio from video to maintain the identity of the participants and used a professional transcription service to transcribe the audio files to text. I started to familiarise myself with data by watching the audio-visual recordings and reading the transcribed text from each session. I replaced all the names with simple alphabetical and numeric codes from the texts: P1 to P10 for ten participants and R1 and R2 for two researchers. I also made a table to write down my initial ideas from each session.

2. Generating initial codes: At this stage researchers code exciting aspects of the data in a systematic order for the complete data set. This stage is about gathering data connected and appropriate to each code and producing preliminary codes. In my study at this

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stage, I formed a new table with my preliminary code in one column and relevant identified aspects in another column next to each code.

3. Searching for themes: At this stage, researchers try to group the codes to create themes. This stage is about collecting all connected and appropriate data for each theme and forming the themes. In my study at this stage, I made a small card with each code written on it and used a clustering technique to group relevant codes under a different theme. Next, I changed the order of the rows from the table with codes to group the relevant codes under each theme close to each other.

4. Reviewing themes: At this stage, researchers review the themes again and check each theme regarding the identified codes and the complete data set. Often this stage aims to create a thematic map of the analysis. In my study at this stage, I moved back and forth from checking the theme to forming the themes and sometimes all the way back to the producing the preliminary code stage. This stage sometimes led me to regroup the codes to make sure they were all connected under a specific theme.

5. Defining and naming themes: At this stage, researchers carry on with analysis to describe the characteristic of each theme and the comprehensive narrative the analysis communicates. This stage is about creating a clear description and name for each theme. In my study at this stage I again moved back and forth between the identified themes and grouping them. The outcome of this stage in my study led to the formation and naming of the meta-qualities.

6. Producing the report: This is the final stage of the analysis. At this stage researchers have the final chance to conduct the last round of analysis. Also at this stage researchers compile a collection of clearly identified examples that connect the analysis to the research questions and literature and write an academic report of the analysis. In my study, the result of this stage can be found at the conclusion of the result chapter as well as the analysis chapter, where I wrote about identified meta- qualities and their connections to the identified concepts from the literature review. The identified meta-qualities in this study provided the answer to RQ3,

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whereas the identified concepts from the literature review were relevant to answer RQ1.

3.6 SUMMARY OF ETHICAL CONSIDERATIONS

This section provides the summary of the techniques to address ethical concerns and limitations.

This research project involved audio-visual recorded focus groups and group interviews with household members:

 Focus group and group interviews at household residences

 Audio-visual recording to gather observational data of this discussion

Ten participants in total across the five households in the greater Brisbane area were sought; two participants per household. I recruited participants through my personal-professional network. Participants lived in five different rental properties in the greater Brisbane area and were aged 18 and older. There was no distinction of gender, political affiliation or nationality.

For each household, the research involved group interview sessions in the households to understand how participants made use of the data physicalisation system to understand energy use. Group interviews took from 60 to 90 minutes.

The group interview sessions followed a similar format. Participants were presented with the data physicalisation system, which they used to map the energy use for their household. This was followed by a discussion and feedback session. The group interviews were held in a setting of the participants’ choosing within their residences to ensure that the discussion of the prototype was as close to an actual-use context as possible (rather than in, for example, a laboratory setting).

Group interviews were audio-visually recorded to document participant discussions and detailed physical interactions with the physical system; it was not feasible to capture this data by other means. In this project, the audio-visual recording involved participants showing how they used and interacted with the system. During this process, participants were asked questions regarding their use of the system and to give feedback on the design. The session was conducted

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in an informal fashion and questions were not personal in nature. Also, participants were informed about what would be recorded during the session, and informed consent was obtained (Queensland University of Technology Ethics Approval Number 1500000899) before they agreed to participate in the research. It was also made clear to the participants that they would be able to withdraw at any time without any consequences.

The project’s primary focus is on collaborative data physicalisation systems and one of the main research questions is concerned with how people employ data physicalisation collaboratively. To address this concern and answer the research question required gathering and analysing a high density of behavioural details. Also, because there is no other ready descriptive vocabulary for bodily behaviour, the audio-visual method was appropriate for collecting and analysing data.

In general, to minimise the risks and address ethical concerns I took steps listed below when visited the households:

 Assured participants that they did not have to answer any questions or take part in any activities with which they were not comfortable

 Facilitated the discussion to direct it where needed

 Made clear to the participants that they would be able to withdraw within a month from participation without any consequences

 Travelled to the interview locations with my principal supervisor

Specifically, to minimise the risk of participants feeling discomfort about having to provide feedback on the design of the physical system, I assured them during the feedback sessions that this research is interested in all possible ideas to improve the system and prototypes and that there were no right or wrong responses. In case participants felt uncomfortable about offering negative feedback, it was explained that to improve the design, critical comments about the prototypes were at least as useful as positive ones, if not more so.

To minimise the risk that participants might feel embarrassed by being recorded, participants were informed about what would be recorded during the session and the reasons for doing so, before they agreed to participate in the

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research as part of the informed consent process. Participants were also able to choose where in their household the video recording took place. The sessions were carried out in an informal fashion and questions concentrated on the physical system and the activity session which were not personal in nature. The focus of the audio-visual recording remained on using the system and participants’ physical interactions with it, rather than on the participants themselves. The audio-visual data was used for this research only, and no recognisable audio-visual materials will be used for any publication.

To minimise the risk that participants might feel discomfort at being asked to discuss details of their energy use in front of other household members and the researchers, the sessions were carried out in an informal fashion in the privacy of their own home. As with the other potential risks identified here, minimising the risk depended significantly on the researchers facilitating the activity to ensure that participants were not made to feel uncomfortable. The energy-mapping activity was carried out at a household level, which, as expected, shared information between household members. The informed consent of all household members was obtained, as well as an agreement from the entire household about where in the household the interviews would take place. Before beginning the design evaluation sessions and group interviews, household members were asked to check if there were any private items they might not want to be recorded. If there were, participants were given the option to remove these from the interview area or direct the filming so that they were not recorded. While this research was interested in collaborative data physicalisation, different household members may have had different perceptions of what affects energy use. The mapping activity and discussion were not focused on reaching a household-level consensus on the relevant factors affecting their energy use.

Design Evaluation and Group Interview Group interviews cannot usually be truly anonymous because the researchers know the identity of the participants and, in this case, participants all knew one another’s identities within each household. To ensure that all participants in the interview had the chance for their voices to be heard, I facilitated the group interviews by making sure everyone had a chance to have

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their say. When transcribed, all statements and answers were made anonymous and treated confidentially. All data were archived securely, as described below:

 Participants were de-identified through a simple lettering/numbering system

 All data, including audio-visual recorded files, were stored in password- protected folders on a computer/server

No identifying elements, including names and personal information, are used in the thesis and/or related outcomes such as publications. Faces and other identifying features were blurred or cut from images taken and videos recorded.

To sum up, a researcher’s goal should be to become flexible enough to identify and use the proper method to answer the research questions (Morse & Richards, 2002). In this project, qualitative methods used to collect data were observation and focus groups as well as audio-visual recording, semi-structured interviews, and field notes. Raw data comprised video and audio files, field notes, and transcriptions, which were analysed using thematic analysis approach.

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Chapter 4: Design Development

This chapter provides a detailed report on the process of designing and making the physicalisation system. After the introduction (section 4.1), section 4.2 provides the detailed account of designing the system through iterations of form making, testing the system, data mapping, brain-storming and reflecting on design. Section 4.3 introduces the context of the use for the design evaluation and iterations of design and making the system to be tested by participants. Section 4.4 presents the design iterations for the final system and planning and designing the design evaluation activity sessions to test the final system with participants in a real-world scenario. Finally, section 4.5 provides a summary of the chapter.

4.1 INTRODUCTION

This chapter provides a detailed report on designing a system suitable for collaborative data physicalisation based on a token and constraint system. This process includes the iterations of form making, planning the workshops, and testing the system with data sets and potential users. The chapter next presents the identified context of use and introduces the recruited participants, followed by design iterations for the final system and the planning and designing of the activity to test the final artefacts with participants in a real-world scenario.

In my previous work, I designed and 3D-printed a static personalised data physicalisation based on my DNA-Profile data. The primary objectives of that project included designing a personalised item and representing data in a tangible and physical form, in other words, Data Physicalisation (Rezaeian & Donovan, 2014).

In this project, I decided to explore other possibilities for physical data representation, particularly in a collaborative use-context. Initially, I started by considering how to design a physicalisation system flexible enough to be able to map different data sets into. I was looking for physical bits that could build a suitable physical visualisation and that had the potential to be used and reused for different datasets over time, embodying the constructive visualisation

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concept as outlined earlier in the literature review chapter, section 2.3.2. Also, in contrast with my previous project, I wanted to design a system with moving parts. Ideally, I wanted to design a system flexible enough to be able to update and change the presentation for multiple uses and different data sets. Moreover, to design a system suitable for use by lay users to address the goals of democratising data visualisation, as introduced earlier in the literature review chapter, section 2.3.1.

In the initial design exploration, the design of the physical units was inspired based on a token and constraint system. Tokens are single physical units that represent abstract data or information. Constraints are forms which mechanically confine how tokens can be manipulated into a variety of configurations (Ullmer et al., 2005).

Token and constraint systems have two fundamental elements: First, tokens or physical objects as a representation of data and/or information and second, constraint or physical manipulations of the tokens as an interactively engaging meaning-making process. In the next section, I explain how the design choices I made relate to designing a suitable token and constraint system for this project.

4.2 DESIGN OF THE SYSTEM

In mathematics and computer science, a binary value is a value expressed in the base-2 numeral (or binary values) system. This system uses only two symbols: 0 (zero) and 1 (one). A system based on binary value is in use by almost all modern computers and computer-based devices. Inspired by the fact that in the 21st century the computer is the primary tool to design and create any kind of visualisation, I set out to design a data physicalisation system that would be capable of representing binary values for the first phase of my design development.

Form Making Designing tokens started with the form-making process. Form-making went through iterations to achieve a suitable design for testing the system in a real- world scenario with real data and a context of use. The initial tokens were in the

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form of hexagons. Hexagons are one kind of geometric shape that can afford tessellation; tessellation is an alignment of forms, particularly of polygons in a repeated pattern, closely fitted together seamlessly without gaps or overlaps. This characteristic was vital because I wanted to design a token and constraint system that could grow spatially. Given the highly physical and visual nature of each iteration, I provide each description with a figure.

I began with the design of 3D models by using computer-aided design software applications, namely Auto CAD and 3ds Max. As a result, a set of flat hexagons 6mm on each side and 6mm high was designed and 3D printed. I explored the potential spatial arrangements of these elements by moving the units next to each other. Since each side had a cubic surface, each unit could be arranged seamlessly next to another unit, both vertically and horizontally. This characteristic allowed for the creation of a physical pattern that could grow in many directions according to the general tessellation idea (Figure 16).

Figure 16.a. Flat hexagon 6x6mm and their spatial arrangement, b. Example of tessellation, c. Actual picture of the unit

As highlighted in (section 2.4.3) token and constraint systems have two different stages of interaction, associate and manipulate. In the associate stage, an individual or a few tokens are associated with a particular constraint configuration. Association can be achieved by locating the token within the physical space provided by a constraint. This action may be undone by removing

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the token. The associate aspect forms a physical relationship between token and constraint. Often token and constraint systems support the associate aspect and only some systems support both the associate and manipulate stages. In the manipulate stage, the token is physically manipulated within the confines of the constraint. In this scenario, tokens were constrained mechanically to be manipulated with a limited degree of freedom either across a straight axis or turnabout on a circular axis. Also, to support the mapping values for a binary system, the token movement must allow mapping of the 0 (zero/off) and 1 (one/on). To address this requirement, I changed the flat surface on the top and bottom of each hexagon to a cone shape. This change created a physical and visual contrast, with positive and negative surfaces for each unit suitable for mapping a binary value of one and zero or on and off (Figure 17).

Figure 17. 6x6mm hexagons with positive and negative surfaces and their spatial arrangement, b. Example of tessellation, c. Actual picture of the unit

After testing the new 3D-printed tokens with positive and negative cone- surface on top and bottom, I realised the tokens were too small and not easy to interact with. Also, the visual contrast between convex and concave surfaces was not apparent. To address this issue, I scaled up each unit to 10mm per side. I also added a hole at the centre of each side to explore different possibilities for connecting each unit to the next to support the tessellation idea in physical form.

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Also, I needed to improve the design to address the associate and manipulate phase more clearly based on the token and constraint system (Figure 18).

Figure 18. 10x10mm hexagons with holes and their spatial arrangement, b. Example of positive/negative caps, with positive and negative surfaces, c. Actual picture of the unit

A token and constraint connection can also be nested. The physical object can perform as a source constraint for one or some associated tokens and at the same time as a child token inside a more significant constraint. Following this concept and inspired by the cone design from the previous iteration, I 3D printed an individual cone shape cap to be nested on each unit and to maintain the option of a positive and negative surface to support mapping values of one and zero (Figure 18).

There were some shortcomings to this design. First, the nested cap idea was not practical, as it was not easy to insert and remove the cap of each unit more than once or twice. This problem was due to the physical characteristics of the materials available for 3D printing at the time. Forcing the cap into the unit could break the unit after the first or second attempt, defeating the purpose of having a positive and negative surface. Second, I still needed to connect and arrange tokens next to each other in a way that maintained their position.

In the next iteration, I used a different approach to apply the concept of nested tokens. I explored the possibility of making each token in the form of a couple. The idea behind this design was to improve the connection between units

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and to address the issue of the cap system identified in the previous iteration. As a result, I designed each token in the form of a pair, one with a flat cap (surface) and one with no cap. In this case, two different surfaces could be distinguished for each unit: the flat bottom surface suitable to map a ‘one’ or ‘on’ value and the top void surface suitable to map a ‘zero’ or ‘off’ value. After testing with 3D- printed tokens, I realised that, similar to the cap issue in the previous design, it was not easy to insert the units into each other, and the unit would break quickly (after a few insertions) due to the limited strength of the 3D-printed tokens (Figure 19).

Figure 19. 10x10mm pair-hexagons with a flat cap and no cap and their spatial arrangement, b. 10x10mm pair-hexagons unit with a flat cap and no cap, c. Actual picture of the unit

The idea to design the units in pairs did not serve its purpose: to improve the connection between the units. Thus, I decided to design the units individually. I realised tokens could move and translate along a linear axis and turn on a rotational axis. Considering these characteristics, I designed a locking system that included one positive extension and five negative connectors for each token. In this case, each unit could attach and associate to the next unit from the side with the extension to any of the five sides of the nearby units (Figure 20). In this situation, each token could be manipulated around its rotational axis with the limitation of being positioned at 90 degrees each time. The rotational manipulation provided a physical and visual contrast suitable to the design goal

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of being able to map a binary system. In this design, the ‘one’ or ‘on’ value could be mapped to the edge side, and the ‘zero’ or ‘off’ value could be mapped to the void surface(Figure 20).

Figure 20. 10x10mm hexagons with one positive extension and five negative connectors and their spatial arrangement, b. Example of tessellation, c. Actual picture of the unit

The connection between units improved but remained an issue when it came to mobility. The locking system was not strong enough to hold the units next to each other without additional support. The next iteration sought to address this shortcoming.

Testing the Initial System to Represent a Data Set Up to this stage I had designed physical tokens that could connect to each other and support mapping at the required variables of a binary system. As one of the main objectives of this research study was to create an adequate system flexible enough to map different data sets and to support the concept of constructive visualisation, I started to design the system before choosing any data set. I used hexagons as an example of more general tessellation geometry to design a token and constraint system that could grow in different directions and create a vibrant physical pattern. To address these requirements, the system needed to be dynamic and flexible enough to use physical tokens to map data, and to be modified, updated, and manipulated when needed.

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Following my initial form explorations for a token and constraint system based on hexagons described in the section above, I needed a real-world data set to test my design approach. I started by looking at open source datasets available online, including those available from the Australian Bureau of Statistics. Weather data are some of the most common data available online and multiple approaches to visualising them exist, for instance, Measuring Cup, E-Cloud and Can We Keep Up, which I analysed in the literature review chapter above. I chose rain data for Brisbane (Table 10), and I decided to use physical tokens to map the basic information of rainy days in Brisbane in 2014. I decided to represent the two options of rain and no-rain during each day using physical tokens.

Table 10. Daily Rainfall (millimetres) BRISBANE Station 2014

Reference: 18754822. © Copyright Commonwealth of Australia 2015, Bureau of Meteorology. Prepared using Climate Data Online, Bureau of Meteorology http://www.bom.gov.au/climate/data

2014 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1st 0.6 0.6 4.6 0.4

2nd 1.2

3rd 1.0 1.6 1.6 13.8 0.2

4th 0.6 4.6 0.2 2.0

5th 3.4 0.4 0.4

6th 0.2 11.6

7th 40.2 1.8 9.2 1.4

8th 0.4 0.2 3.0

9th 0.2 1.2 25.2

10th 1.4 2.2 0.6

11th 4.6 0.2

12th 0.8 27

13th 3.6 1.4 9.2 3.8

14th 8.8 0.8 3.2 0.2

15th 0.6 0.6

16th 0.4 0.8 4.0

17th 0.2 4.6 1.2 2.0 51.6

18th 8.4 0.2 0.6 0.2 2.6 1.2

19th 0.2 0.2 13.8 16.8

20th 0.6 5.8 56.0

21st 0.2

22nd 2.2 0.6 1.2 5.2 3.2 0.6

23rd 2.2 0.2 2.4 22.4 3.0

24th 94.0 1.2 0.6 0.2 6.0 0.8

25th 2.0 1.4 22.4

26th 1.2 21.6 0.2 9.8 3.6

27th 5.6 8.0 3.0 7.2

28th 82.6 2.6 0.2 3.6 10.4 2.6 43.4 22.4

29th 0.8 3.2 0.4 1.6 1.0 6.0

30th 0.2 0.8 0.2 1.0

31st 2.2 0.6

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Physical Tokens to Represent Rain-Data in Brisbane To present a year’s worth of data on a daily scale, this project needed at least 365 tokens to represent each day individually. To represent a week required seven tokens arranged next to each other, and to represent a month required connecting between 28 to 31 tokens (Figure 22). On a non-leap year, February has 28 days, September, April, June and November have 30 days, and the remaining other months have 31 days. On a leap year, February has 29 days. Based on these facts and considering the issue regarding the connection between the tokens from the previous iteration, it was necessary to make sure the tokens would hold their position strong enough to represent the mapped data. Also, connecting and arranging the tokens and being able to manipulate them was crucial in the sense that it was essential to be able to update and change the mapped data to support the constructive visualisation concept as well as to address the requirement of creating a token and constraint system.

The Lesson from The Abacus To address this issue, I looked at the example of the abacus, which is an excellent example of such a token and constraint system. An abacus represents information as individual beads, as well as the spatial formation and arrangement of the individual components (tokens) within the physical constraint of the counting board and bars. The practical needs of mobility, managing and maintaining many physical components caused the abacus to develop over the years into a system with “captive” beads (tokens), nevertheless tokens stand to be movable and spatially reconfigurable. Based on this example, I decided to design sets of captive tokens to present rain data.

Tokens can also be moved and relocated between constraints to convey a different meaning. Sometimes constraints include different kinds of physical tokens. In these cases, the absolute and relative physical location and arrangement of tokens, both in relation to the constraint and to one another, can be mapped to different interpretations. For example, in the context of time over the year, seven tokens next to each other can represent one week of data, and a set with 31 tokens can represent one month of data. One common form of constraint mapping is the use of racks; in this approach physical tokens are

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manipulated within a linear constraint (Ullmer et al., 2005). For my rain data physicalisation, I decided to present the rainy days on a monthly scale. This meant creating 12 sets (racks) consisting of 31 captive physical tokens. For the months that had fewer than 31 days, I decided to keep the extra few tokens in the set, but to leave them flat or void to represent that there was no data mapped to them (Figure 22).

The design of each physical token for this physicalisation included a bigger positive extension at one side and a matching negative connector at the opposite side to provide a better locking system. Each token also had a flat surface at the bottom and a void at the top to provide the minimum requirement for mapping ‘one’ (rain) and ‘zero’ (no-rain) variables. I also scaled down the measurement of each token from 10mm at the side to 9.238mm to achieve 16mm length from side to side, matching the standard 4x4 Lego brick size. I chose Lego brick measurements as a reference because Lego bricks are familiar physical objects and designed to be physically manipulated. This was important to achieve a suitable design for a physical token that users would recognise as a manipulable object (Figure 21).

Figure 21.a. Lego size hexagonal unit with positive extension and negative connector, b. From a top perspective and from left to right units represented: 1. Void or Empty, 2. Void- Uppermost, 3. Mass or Flat, 4. Flat-Uppermost. c. Actual picture of the unit

After arranging the tokens for each rack, I secured each token in its position by passing a transparent, stretchable vinyl line through all 31 tokens and tied

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both ends for each set. In this scenario, each token can be unlocked by separating from its neighbouring tokens along with their linear axis and turned on its rotational axis. In this case, because of the resistance of the stretchable vinyl line, tokens were forced to pull back towards each other as soon as they got separated from their neighbouring tokens. This helped the manipulated token to maintain its new position and pull back to the lock position with its neighbours. In this iteration, each token can represent four different arrangements, as shown in (Figure 21).

In designing the physicalisation of the 2014 Brisbane rain data based on a token and constraint system, I mapped the data to physical tokens. Each token (3D-printed hexagon) represented one day of the year. I went through Table 3, month by month and day by day. I decided that if the represented day was rainy, I would manipulate the token by unlocking and turning it about its rotational axis to present the edge with a void. If the chosen day was a dry day, I rotated the token to show the mass or flat side (Figure 22). As I mentioned earlier, I divided the days on a monthly basis; this means I mapped the data for each month separately, in 12 sets of 31. Then I arranged them from January to December (top to bottom) and from first to the end of the month (left to right), as shown in Figure 22. I had the option to rotate the tokens towards the end of the months with less than 31 days and choose the flat-void side to highlight shorter months. However, at this early design phase, I choose to maintain the harmony between solid-flat and edgy-void surfaces. The rationale behind this decision was to avoid introducing a new variable to the system. As a result of this decision, the months with less than 31 days remained with solid-flat tokens similar to no-rain days towards the end of each month.

To complete the design of the physicalisation that represents Brisbane rainy days over 2014, I created a physical constraint made of MDF-boards using laser-cutting technology. I associated each rack from January to December (top to bottom) and from beginning to the end of the month (from left to right) (Figure 22). The physical constraint supported mobility, managing, and presenting the physical tokens.

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Figure 22. Data mapped based on Table 10;

From January to December (top to bottom) and from beginning to the end of the month (left to right). The rings at the ends of each vinyl-line are to secure each rack position in the MDF-board.

Creative Exploration with Peers as a Part of the Design Development I conducted an informal internal creative exploration session with my fellow researchers from the Design Lab at QUT to brainstorm the user experience, discuss the set-up of the study, and explore the effectiveness of the Brisbane rain data physicalisation. The purpose of this session was to solicit my colleagues’ expert advice and feedback on the design before proceeding to a qualitative evaluation of the design with end users.

Eight fellow researchers attended the session. I set the session around a conference table and formed four groups of two. I divided Table 3 of rain data into four parts. As a result, each group was provided with three months of rain data from Table 10 in the form of a printed table on paper. I also provided three sets of physical tokens for every group: each set included 31 hexagonal tokens arranged in a linear format and tied with a stretchable vinyl line in the centre. I gave a brief explanation of the design and demonstrated to my colleagues how each physical token could be unlocked from the provided sets and manipulated by rotation. I demonstrated how to choose the edge with a void to represent a

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rainy day and the solid or flat side to represent a dry day. Next, I demonstrated how each mapped set could be associated with the physical constraint designed and made of MDF boards, which were presented at the centre of the conference table. At this stage, I asked colleagues to map the rain data from their group’s printed table to the sets of physical tokens and arrange them accordingly within the physical constraint (MDF board) as demonstrated. During the creative exploration session, colleagues were freely communicating within and between groups. After mapping and arranging the tokens, we held a group discussion where I asked colleagues to give their feedback and ideas based on their interactions with the system. The potential issues such as whether there may have been confusion between the use of the same solid flat tokens for non-rainy days, or between short and long months were not noticed or tested during this session.

Design Insights Valuable insights were gained during this informal creative exploration session. One suggestion from colleagues was that the artefact could act as a coffee table object. Colleagues generally indicated that the object was easy to use. A couple of colleagues commented that manipulating and mapping the data was easy and fast. Others talked about how enjoyable and fun putting pieces together was. Some of the colleagues made a comparison between the activity and organising and sorting tasks and highlighted the visual and physical contrasts before and after mapping the data to the system.

Shared Physical Tokens for Shared Interaction From the creative exploration session, I also realised that shared physical tokens for shared interaction could be a powerful collaborative tool while, at the same time, the openness and playfulness of a designed artefact based on a token and constraint system might help users to concentrate on the task at hand, such as mapping the data.

I planned the session with an open mind to observe, experience and brainstorm the overall potential of the data physicalisation approach. Interestingly, the presence of factors and effects regarding collaborative uses based on the token and constraint system were strongly apparent. This was

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aligned with the findings of Ullmer and Ishii adopted from Hornecker’s work: “tangible interfaces’ support for group communications appears to be one of their most apparent and compelling virtues” (Ullmer et al., 2005, p. 96). Some of the critical factors and positive effects summarised by Ullmer et al. concerning cooperative uses and collaboration of physical interfaces are summarised in Table 11.

Table 11. Factors and Effects summarised by Ullmer for Cooperative Use of TUIs;

adapted from Hornecker’s work (Hornecker, 2002; Ullmer et al., 2005, p. 96)

Enabling Factors Positive Effects

constant visibility externalisation (active participation)

bodily shared space intuitive use (gestural communication)

haptic direct manipulation awareness (provide focus)

parallel access performative meaning of actions

Facets with special ties to the token and constraint approach are shown in underlined text.

As a result of this informal creative exploration session and my reflection on the design development up to this point, I decided to identify a collaborative context of use for a focus group to more fully evaluate the collaborative aspects of the data physicalisation. With this collaborative context of use in mind, I would design a workshop to use physical data presentation based on a token and constraint system with end users. I needed to know who the user and the context of use would be to be able to design more engaging interactions and meaningful experiences.

I planned to evolve the design by improving the system to assist the participants to map their data, keep the conversation going, and reflect on the data and process. Ideally, I wanted the artefacts to enable the participants to make sense of their data and experience the meaning-making process together.

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4.3 CONTEXT OF USE:

Token and Constraint System and Households’ Energy Consumption

Moving from an internal session to a real-world setting required a live and collaborative project working with data sets that could be presented physically with a token and constraint system. CitySmart’s Low Income Energy Efficiency Program was sponsored by the Department of Industry and Science (LIEEP). This program was designed to involve 1,000 participants aged 18-35 who were rent payers in the greater Brisbane area to save energy. So far, this program had focused on digital solutions, such as using a mobile app. The physical approach of this project aimed to provide a new way to tackle energy efficiency.

In this project, I decided to look at the situation from a different angle. Unlike the typical approach to address the energy efficiency issue by updating or changing the technology in the household, I wanted to focus more on people’s awareness of their energy consumption in the household. I thought that updating to more advanced, economical and environmentally-friendly technology might not be the only solution to improve or optimise energy consumption. I considered that adopting some of the suggested technologies, such as solar panels, involves some cost, and furthermore, low-income householders are mostly rent payers, and they do not have the authority to make such changes. I believed making a change in low-income households, like any other household or social group, requires all the members to be involved and to take responsibility. Therefore, I decided to design a collaborative data physicalisation based on a token and constraint system which would be user-friendly and simple, yet practical and efficient enough to engage all the members in the household and help them to make better decisions about their energy use together. This decision was also aligned with the concept of agency and relating to personal responsibility and a degree of ownership for individuals within the households. Following the main objective of the CitySmart project, this study aimed to recruit participants among rent-paying households within Brisbane but, unlike the CitySmart project, the economic background of the households was not considered.

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Getting Familiar with Energy Consumption at the Household Level To have a better understanding of energy consumption at the household level, I started to look at and track different personal energy consuming activities over the course of two weeks at my household. As I live in Brisbane with my family and we are rent payers, I thought this practice could help me to gain more a precise understanding of the context of use. Also, this experience could provide the opportunity to situate me as a designer in the position of a user. I looked at five different energy-consuming activities, including 1. cooking, 2. taking a shower, 3. doing laundry, 4. working on the computer and watching TV, and 5. using heating and cooling (air-conditioning). I also kept track of the time that I was sleeping or was not at home.

Before designing a new token and constraint system for this physicalisation, I considered the available tools. I used Lego bricks to map and track my nominated energy-consuming activities. This test used a grey Lego pad underneath, a column of white Lego bricks at the left to indicate the time, and a row of white Lego bricks at the front to indicate the days of the week. The column of 12 white 2x1 Lego units on the left indicated the hours of a day from 00:00 to 24:00, from bottom to top. The row of 7 white 4x1 Lego units at the front indicated days of the week from Monday to Sunday, left to right (Figure 23). Different colours represented different energy-consuming activities as follows:

 Red: Cooking

 Blue: Taking a shower

 Black: Working on the computer and watching TV

 Vertical Orange-Right side: Using air conditioning

 Vertical-Green-Left side: Laundry

 White: Sleeping

 Blank: Away from home

After mapping the data for the first week, I noticed some of the activities happening simultaneously. For example, I used air conditioning when I was working on my computer or watching TV, and sometimes the washing machine

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continued to work when I left home. As a result, the system needed to be flexible to map more than one activity at a time. Therefore, I used a vertical arrangement on each day next to the mapped activity on the Lego pad to map parallel activities. For example, on Monday after I woke up, I turned on the washing machine and took a shower before I left home. The washing machine was working while I took a shower and after I left home.

Figure 23. Week 1 mapping five different energy consuming activities at my household

Looking at my energy-consuming data mapped based on the six chosen categories over a week (Figure 23) allowed me to reflect on some of the limitations of this approach. Even though Lego bricks are physical bits, I used them to map data in a 2D manner on a flat surface. I did not map any data or information in a third dimension/the Z-axis. Lego bricks are physical objects, and they can act as physical tokens, but I found it challenging to create physical constraints using Lego bricks in my approach. Lego bricks can create almost seamless structures, and the Lego pad was too open, without physical limitations that would be suitable physical constraints. I marked the Lego-pad by using Lego bricks to indicate the time of the day (left column) and day of the week (front row), but this only created a visual reference for mapping the activity. I could put Lego bricks for mapping anywhere on the Lego-pad without much physical limitation. One of the significant issues was the missing relation between token and constraint. I found the Lego mapping visually interesting but not physically rich enough. At this stage, I asked the question: what are the advantages of the Lego bricks approach compared to the equivalent illustrated version on paper or screen (Figure 24)?

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A/C Cooking Laundry Shower PC / TV Sleeping Away

24:00

22:00

20:00

18:00

16:00

14:00

12:00

10:00

08:00

06:00

04:00

02:00

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Figure 24. Illustrated version of Lego-board on (Figure 8)

I recalled the richness of the mapping process and interacting with Lego bricks on the board, but the missing part was the absence of the physical constraint. On the second week, I decided to create a unique physical constraint using the Lego bricks and board to create a new task to map some data in a third dimension (the Z-axis). After looking at my first-week energy-consuming activities, I decided to have a closer look at the laundry category. I realised I was mapping mostly personal energy-consuming activities for every other category, but I was in charge of doing laundry for my family. I divided the laundry category into two activities, using the washing machine and ironing. On the second week, if I used the washing machine, I mapped a green vertical Lego brick to the left side of the day according to the day and time of the activity. If I did ironing, I mapped a green vertical Lego brick to the right side of the day according to the day and time of the activity. I also added a standing column in front of each day and used

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a small plastic loop to indicate how many times a day/week I have used the washing machine (Figure 25).

Figure 25. Week 2 mapping six different energy consuming activities at my household with vertical columns for a personal goal

By adding the standing column in front of each day, I created a physical constraint, and white plastic loops were physical tokens. The relation between token and constraint was clear, and it was easy to identify the days that I used the washing machine. Also, to add and remove plastic loops offered an easy and obvious visual and physical contrast that helped to map the data. To avoid conceptual confusion, I used taller 1x2 Lego bricks to build the standing columns. Also, I added the standing columns on top of the longer 1x4 units in front of the board which they indicate the days of the week. The reason for choosing this location and taller bricks was to maintain visual and physical difference between the column and the standard 1x2 Lego bricks used as a unit to map the data on the board. Ideally, I wanted these columns to be seen as a part of infrastructure.

Containers, Tokens, and Tools Following terminology from the literature review chapter, section 2.3.3 Token and Constraint and the theoretical standpoint of this project, so far tokens were used mostly as containers, both the hexagonal tokens in the rain-data example and the Lego bricks in the energy consumption test. However, after I compared the Lego approach with the illustrated version on the computer, I realised the potential of labelled tokens for days of the week and time of the day. These iconic tokens seemed potentially helpful both for mapping and readability (Figure 24). The idea of labelled tokens was in line with iconic forms of

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representation, in this manner serving as tokens by Holmquist et al. (Holmquist et al., 1999). One conclusion was that future systems both in this study and in general, will likely adopt tokens to serve a variety of roles.

Mapping on the Third dimension, the Z-Axis Building on the experience of using plastic loops and exploring the potential of mapping data in the third dimension/Z-axis, I developed my ideas further by designing and laser cutting seven columns to represent the seven days of the week and different basic geometric shapes to represent different energy- consuming activities (Figure 26). I chose a triangle shape to map the use of the shower and a circle shape to map the use of the washing machine (Figure 26). After mapping these two energy-consuming activities for a few days, I realised the limitation of this approach.

Figure 26. Seven columns to represent seven days of a week;

From left to right columns are to represent Monday to Saturday. Triangles represent taking a shower and circles represent using the washing machine. Figure 11 reads as below: Monday: 2 Showers, 1 Laundry, Tuesday: 2 Showers, 1 Laundry, Wednesday: 2 Showers, Thursday: 2 Showers, Friday: 1 Shower, Saturday: No data, Sunday: No data.

Design Insights In the previous attempt with Lego bricks and plastic loops, I had only one variable to map in the third dimension: the use of the washing machine (Figure 25). In the new test, I was mapping more than one category in the third

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dimension, and with this design, it was not easy to recognise tokens’ different geometry when they were stacked on top of each other. For example, a circle could easily hide between two rectangles. The primary goal of this test was to explore the potential of mapping in the third dimension/Z-axis. The simple geometry designed for this test’s tokens was too thin, and this was due to the limitations of the laser cutting technology and material available. I used 3mm MDF board for this test, and as a result the shapes did not have sufficient thickness in the third dimension to be visually recognisable. This experience was a valuable lesson regarding visual affordance while designing physical tokens and informed the design of the final tokens.

4.4 DESIGNING THE FINAL SYSTEM AND FINAL ACTIVITY SESSIONS

Design of the Tokens Inspired by my previous design with hexagonal forms to create a tessellation geometry and the test with Lego bricks, I designed magnetic cubes that could connect to one other from each of their six sides (Figure 27). I 3D printed the cubes in seven different colours (Yellow, Orange, Red, Blue, Green, Black and White) and based on the dimensions of a 2X2 Lego brick (Figure 21). As I mentioned earlier, I chose Lego brick measurements as a reference because Lego bricks are familiar physical objects and designed to be physically manipulated. This was important to achieve a suitable design for a physical token. Also, from the test with MDF boards and geometric shapes, I learned that two of the key affordances of physical tokens are that they can be recognised and manipulated both visually and physically. I used 3D printing technology to produce physical tokens even though it was more time-consuming compared to a laser cutter, as 3D printing had fewer limitations regarding measurements and more options to choose from (a variety of colours and materials). To address the constructive visualisation aspect of the project and to be able to reassemble the physical tokens frequently, I made the cubic tokens magnetic. Every token has small cubic magnets inserted into the centre of each side to provide a magnetic connection (constraint) between physical tokens from all possible directions. This was an important characteristic to support both the constructive visualisation approach as well as the constructive assemblies concept described

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by Ullmer and Ishii (Ullmer et al., 2005). Unlike ordinary Lego bricks, magnetic tokens connect to each other from side to side. Physical cubic tokens were 3D- printed out of seven different colour plastics to provide the option to map up to seven different variables, in this case, seven energy-consuming activities. The plastic material and flexibility of the cubic tokens also made it possible for small 3x3mm magnets to be inserted at the centre of each side.

Figure 27. Magnetic Cubes in seven different colours

Design of the Constraints In designing a token and constraint system, designing the constraint is as essential as designing the tokens. The phrase ‘token and constraint’ signals the tight interdependent connection between these two components. Tokens and constraints are complementary, and they must be integrated to create meaningful expressions. This involves both operators and operands. The complementary characteristic between token and constraint should be apparent even when they are physically separated. The unique token and constraint physical relationship should allow them to indicate possible combinations and substitute usage even when they are separated.

Perhaps in the previous tests with Lego bricks (Figure 23 & Figure 25), this connection between Lego brick as a physical token and constraint was missing, Lego bricks could be mapped anywhere on a Lego pad, and the constraints were not closely mapped to the structure of the task I was using them for.

To design the physical constraints for the final system, I started to design the constraints visually. I designed a weekly chart with individual rows for each day, and each row was divided to four time-zones from left to right, including Early Morning, Morning, Afternoon/Evening and Night (Figure 28). From my

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previous experience and reflection on testing with Legos (see Figure 23 & Figure 25 and an illustrated version in Figure 24), I decided to label the days of the week and simplify the 24 hours of the day into four time zones.

Figure 28. Weekly Chart, days are divided into four time zones from early morning to night

I mounted the chart on a small whiteboard with a stand to make it easy to store or display and suitable for magnetic tokens to be mapped on the chart (Figure 29). This design provided a potential platform for a household member to map his/her energy-consuming activities over a week. In this case, each colour magnetic token could represent a different energy-consuming activity, and a household member could map his/her energy-consuming activity by adding a token to the chart accordingly. For example, if yellow cubes were used to represent the use of lighting, household members would add yellow cubes to the chart when they used lighting, according to the time of day and week.

Figure 29. Weekly Chart mounted on a whiteboard with stand

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In this design, there was more than one way to use the chart in the households. One way was to provide each household member with an individual chart similar to what I experienced personally with Lego bricks or another way was to use the chart for the household as a whole and involve all the members of the group. To develop the idea further and test it in a group, I conducted a second internal creative exploration session with my colleagues.

Testing with my Peers as a Part of Design Development I conducted another internal session with my fellow researchers from the Design Lab at QUT to brainstorm, test user experience, and study and explore the design of the chart with magnetic tokens. The purpose of the session was to further develop the design and format of the workshop activity before carrying out a formal user study with end users. The fact that I needed to develop both the physical design and the design of the workshop activity was why both these parts were included. The session was not undertaken to run the test on my colleagues, but to get expert feedback from them on how the session with end users could run.

Design Insights One of the critical design insights from the previous session was that I realised shared physical tokens for shared interaction could be a powerful collaborative tool. Furthermore, from this creative exploration session with my peers using the chart and magnetic cubes I learned that it would be more practical and engaging to make the workshop activity more collaborative and participatory.

I found my experience with Lego bricks and mapping my activities that consume energy somewhat dull, and it was a challenge to stay motivated to go through energy mapping on a daily basis. Another observation was that if I forgot to map a specific energy-consuming activity almost immediately, it would be challenging to remember later and almost impossible to remember after a day or two. (During the weeks of mapping personal energy-consuming activities using Lego bricks, I kept a personal journal to make sure I remembered everything).

For the internal session, the weekly chart was redesigned with an individual column for each household member and a household column with the aim of

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household members sharing the board (Figure 30a). The household column was designed for energy-consuming activities that were shared among household members, for example, if someone cooks for everyone or uses the dishwasher after a party or family meal. I tested the idea with four groups of two. I asked colleagues to map their nominated energy-consuming activities over the last 48 hours onto the board using physical magnetic tokens. Each colour represented a different energy-consuming activity (Figure 30).

Figure 30. Chart with individual columns for two colleagues and house for the last 48hr (separate column for each day and divided into four time-zones)

Half-way through the session, after colleagues finished mapping their data on the board, I asked them to imagine a scenario in which they lived in the same household (this was just an informal session, and colleagues were not living in the same household). I asked colleagues to look for overlaps or similar activities at the same time, and if they could combine their individual activities and move to the household column. This activity was designed to test the idea of how participants could collaborate.

This creative exploration and feedback session with my colleagues helped me to clarify for myself what the possible purpose of the mapping activity was and highlighted some further essential questions and insights about how the session with end users should be run.

From the observations and conversations during this session, some of the essential concerns to the design of this study were raised. Concerns like, is this

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approach about saving money or saving the environment? For example, if this is only about saving money, colleagues were suggesting things could happen elsewhere, such as at work instead of at home, when they do not pay the bill. For example, for charging devices, one could charge his/her devices at work or charge a power bar and take it home, or even take a shower at work.

Other concerns emerged, such as do the cubes represent a different amount of energy? How much detail should be considered when participants map their data? How can we distinguish using hot water for 5min or 1hr?

All of these questions and observations from the two internal sessions assisted to iteratively refine the research questions and emphasise the collaborative aspect of the study. I was clear on how to create a session that could facilitate participants engaging with the physicalisation process and assist them to ask their questions and reflect upon the core issues of this study. I decided to design the final system to facilitate a meaningful conversation and reflection session about how we use energy at the household level rather than how much we use. I did not want to design an experience for participants to feel like they were competing, being judged, or blaming each other about who used more energy. I thought the final workshop could be called “paying more attention to your energy consumption” or just “paying attention to your energy use”. With this exercise, I wanted participants to try better understand their routine in their energy-consuming habits and be able to look at their energy-consuming activities as a group, at the household level, and perhaps decide where they can make changes together. Each household member could have a different personal goal; by talking about their goals, he or she might come out with a few shared goals at the household level. These goals could be financial, saving money at the end of the months studied, or environmental, or both. I was not concerned with ‘how much’ but rather with ‘what’ and ‘how’ questions.

Design of the Board and Finalising Physical Constraints One of the limitations of the previous design (Figure 29) was that if I wanted to add a column per person per day to map energy-consuming activities over a week, I needed to design a much bigger chart and board. The board grew even more as the number of household members increased. This was not ideal, since I

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wanted to keep the design compact and mobile and to move it around if necessary. Mobility, in particular, was critical, as I observed through the literature review chapter most of the physicalisations did not support mobility, and some of the designers highlighted mobility as an issue to be addressed, for example, Samuel Huron’s work “Building Visualizations with Tokens” (2014). I also needed to be able to pack the set quickly after each activity session, to move from one household to another. Thus, I redesigned the chart for the final system. The final design includes nine columns and nine rows (Figure 31.a.). In this case, each of the seven middle columns represents one day during the week, and each of the seven middle rows represents a different energy-consuming activity.

Figure 31. a. 9x9 chart, seven middle columns represent days of the week, and seven middle rows represent different energy consuming activity. b. Seven columns in the middle from left to right indicate days of the week from Monday to Sunday and seven rows in the middle from top to bottom can represent up to seven different energy consuming categories C1 to C7. The remaining two rows, one at the top and one at the bottom and remaining two columns, one at the left and one at the right, are reserved for labelling each row and column.

The final chart supports mapping up to seven different energy-consuming activities and encourages participants to map their energy-consuming activities together more collaboratively. The focus of the activity is on the energy- consuming activities at the household level (as a group), and each member contributes individually and collaboratively to form the household’s data presentation. This design provided the basic visual outline for designing physical constraints.

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It is worth mentioning that physical constraints are manifested as physical formations which mechanically route how their associated tokens can be manipulated within their boundaries; restricting the movement of each discrete associated token to a limited physical degree of freedom. The final board design contains nine columns and nine rows, seven columns in the middle from left to right that indicate days of the week from Monday to Sunday, and seven rows in the middle from top to bottom that can represent up to seven different energy- consuming categories. The remaining two rows, one at the top and one at the bottom, and remaining two columns, one at the far left and one at the far right, are reserved for labelling each row and column (Figure 31.b.).

As I covered earlier, the use of racks is a persistent form of constraints mapping; in this approach physical tokens are manipulated within a linear constraint. Inspired by this concept and the constructive visualisation approach, I decided to make each row separable from the main board to provide the opportunity for comparing, shuffling, and studying each category individually or in a group (Figure 32). On each row (rack) I used a laser cutting machine to engrave seven squares to indicate the areas for physical magnetic tokens to be mapped. I also manually inserted a small magnetic cube into the centre of each engraved square to create a physical constraint for physical magnetic tokens to be mapped accordingly. With the same token, I engraved a circle shape on the far- left space of each row and manually inserted a magnetic cube into the centre to secure each rack’s position on the main board using magnetic force. The engraved circle can be used to label each rack when separated from the main board. I used the far-right space on each row to laser-cut a circle shape and create a grip for each rack to be detached and attached to the main board. The main board was made of three layers of laser-cut MDF held together by small magnetic cubes manually inserted in each layer and seven individual rows (Figure 32).

Figure 32. The final board made of MDF and Magnets

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Design of the Tip-Cards, Energy Consuming Categories and Phicons Similar to card games, cards can add components such as enthusiasm, curiosity and surprise to the activity. Also, adding textual content to the cards created an opportunity to provide information according to the context of the activity (Mora et al., 2015). For instance, in this project the textual content on the cards provided tips for energy efficiency.

Moreover, the physical characteristic of cards can support playful interaction, and cards can be retained by participants for future remark and reminder (Mora et al., 2015). In this project and during each activity session participants were able to choose and keep as many tip-cards as they liked for their future reference.

Energy Consuming Categories Based on my personal experience from testing with Lego bricks, observation and feedback from colleagues during the internal test, the City Smart project (‘CitySmart - Brisbane’s Sustainability Agency’, 2017), the Reduce Your Juice App (‘Reduce Your Juice on the App Store’, 2017), as well as studying the saving energy webpage published by Brisbane city council (‘Saving energy’, 2017) and energy efficiency tips from Origin (Energy, 2017), I categorised the energy-consuming activities for the final design into six+one categories, as listed below. The summary of my findings from these resources for energy efficiency tips is attached in (Appendix 1).

 Heating and cooling

 In the kitchen

 Hot water

 Lighting

 Standby power

 Laundry

 *Other

* Other could be any other energy-consuming activity that participants nominate, that they would not categorise under any of the six categories listed

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above. This could support the flexibility of the design and promote participant engagement.

For the final design, I assigned a different colour to each category: orange for Heating and Cooling, red for In the Kitchen, blue for Hot Water, yellow for Lighting, black for Standby Power, green for Laundry, and white for Other (Table 12).

Table 12. Assigned colours for each energy-consuming-activity category

Energy Consuming Activity Assigned Colour

Heating and Cooling Orange

In the Kitchen Red

Hot Water Blue

Lighting Yellow

Standby Power Black

Laundry Green

Other White

Tip-Cards I designed seven sets of tip-cards, colour-coded by category. On the back of each card I designed a pattern inspired by the magnetic tokens’ shape (Figure 33), and on the front, each card contained a small logo and a message about energy efficiency tips (summarised in Appendix 1) that matched the colour-coded category. I designed six different sets of tip-cards for the six different energy- consuming categories identified above and an additional set with a white colour pattern for the Other category that participants would nominate. As with the idea of designing the white colour tokens, participants had the potential to contribute to the white colour card set, if they had any tips for their nominated activity. This could support the flexibility of the design and promote greater participant engagement.

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Figure 33. Top: coloured pattern inspired by the tokens for each category.

33. Bottom: cards with small logo and energy efficiency message according to each category

Phicons Phicons are physical objects with permanent attachments. I designed seven physical tokens to label each day of the week. Each token was designed and 3D- printed out of plastic (Figure 34).

Figure 34. Phicons; Top: top surface with engraved text, Bottom: bottom surface with an embedded magnet

Each phicon has a round shape, is 16mm in diameter and 8mm in height, with engraved text indicating the day of a week at the top surface. Similar to the cubic tokens, I manually inserted a small cubic magnet 3x3mm to the centre of

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the bottom surface. The round shape and the embedded magnet assist phicons to associate with the top and bottom label rows designed on the board (Figure 31.b.), and this connection can be considered the physical constraint between phicons and the main board.

The Final System and the Design Evaluation Activity Sessions The final design aimed to encourage participants to map a week’s worth of their energy data on a physical board designed based on a token and constraint system in a collaborative session. I wanted to facilitate a session for household members to create a data physicalisation and engage in a conversation about their energy-consuming activities while using a physical token and constraint system. Also, I wanted participants to be able to reflect on their actions, look at the tip-cards, and decide about their household’s energy consumption habits together.

The final activity was made up of two parts. Part one included mapping the energy-consuming activities to the board, and part two included reflecting on and relating to the tip-cards. The breakdown of steps in the activities over parts one and two can be described as below:

 Introducing the system, including the board, magnetic tokens, and the deck of tip-cards

 Demonstrating how magnetic tokens can be added to the board to map specific energy-consuming activities according to category and day of the activity

 Mapping energy-consuming activities over the last week to the board using the tokens by participants

 Confirming with participants if the created data physicalisation represents their household energy consumption activities over the last week

 Asking participants if they want to make any changes before we move to part two of the activity

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 Introducing, reflecting on and relating to the tip-cards, and asking participants to choose a category to start with and go through each set of cards one by one

 Making sure participants feel free to choose and keep any tip-cards they like, or feel are relevant to their household

 Rewarding participants by giving them the chosen tip-cards and matching colour tokens

 Providing overall feedback and asking participants about their experience of the activity

Figure 35. Activity in progress with household members

On part one, I first introduced the system and demonstrated how the physical magnetic tokens could be added to the board according to each energy- consuming category. Next, participants would go through each category and map their energy-consuming activities accordingly. They would help each other to map the data on the board by adding the appropriately-coloured tokens to the relevant column according to each category. In this case, the time of the activity would not be relevant anymore, and participants could focus on thinking of what energy-consuming activities they did on each day. The primary goal was to collaborate to build the household-energy activities data physicalisation and to have a conversation while mapping (Figure 35). After mapping, participants would have the opportunity to look at the mapped tokens on the board and reflect on the physicalisation, perhaps highlighting the area that was a concern for

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household members either individually or as a group. The activity aimed to facilitate the conversation and process of making the data physicalisation and to give participants a chance to highlight and reflect on some of their concerns about energy consumption in their household. For example, someone might highlight that, either individually or at the household level, they are overdoing specific energy-consuming activities or do something as simple as just showing interest in knowing more about how they could optimise their energy consumption behaviour or learn more. This is when the second part of the activity – reflecting and relating – took place, with the introduction of the tip-cards. As I mentioned earlier, I designed sets of colour-coded cards according to each energy- consuming category and included my findings (Appendix 1) for energy efficiency tips in the form of a message on the back of each card. During the second part of the activity, participants were guided to separate each rack of tokens mapped under a different category and go through the tip-cards from the associated colour-coded set. Participants were encouraged to go through all the sets of tip- cards one category at a time, and they were welcome to collect as many cards as they liked if they felt the cards were relevant to their household or would be useful for them to consider in the future. They were also rewarded by giving the matching colour tokens for their chosen tip-cards. The idea behind the two parts of the activity was to facilitate the participants’ thinking about their energy- consuming activities during part one without having an agenda and thinking where they need to make changes or how they are going to save the energy. In the second part of the activity, participants could decide on the categories or energy-consuming activities they would like to know more about; then they could choose and keep as many tip-cards as they liked to address any of their concerns.

Ideally, I wanted to help the participants to identify the area of energy consumption that they would like to know more about and then provide some feedback in the form of the tip-cards.

Some of my Thinking at the Time of Designing the Activity It seems we easily forget about our routine daily activities, the sorts of things we do without much interest and excitement. Tasks become mundane and hidden in our day to day life. Perhaps a big part of our inefficient energy-

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consuming activities is a part of this daily mundanity. As a result, it seems easy not to realise or to forget about these energy-consuming activities. As a reminder to myself as a designer of this activity, the goal should not be about how much energy has been used on a particular day or time, but rather to design a physicalisation that will help to highlight users’ energy consumption habits and behaviours and the will to improve.

4.5 CONTRIBUTION TO KNOWLEDGE

Throughout the design process reported on in this chapter, the concepts of ‘constraints’ and ‘tokens’ have been central to the design experiments undertaken. The terminology ‘token’ and ‘constraint’ has been used to demonstrate the strong interconnected link between the two components. Tokens and constraints should be united to create meaningful expressions; this includes both operators and operands. The experiments with graphics, hard borders, magnetic forces, and variations in shape reported on in this chapter provide insights into the ways that concepts of tokens and constraints can be employed within a physical data visualization system.

When tokens and constraints are physically separated, their physical and visual forms often need to indicate possible combinations and different usage scenarios passively. Designing for this characteristic can be supported by using the concept of affordances, that is, by giving careful consideration to the affordances of both token and constraint communicated by the design. This can include aspects such as similarity of shape, colour and other visual properties. In this project, I employed shapes which supported tessellation. This allowed for the seamless arrangement of geometrical units to fill a space. Due to the tessellating property of the shapes chosen, the physical and visual form of the tokens was inherently related to how they could be arranged. In summary, the design for the affordances of the tokens and constraints and the design on how to interact with them informed the design of the final system.

Two important aspects of the interaction in relation to tokens and constraints are the ability to associate and manipulate elements with them. In the system used to represent rain data, each hexagonal token was associated with its neighbouring tokens through a physical locking system. The physical locking

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system supported the user in creating associations and also made it possible for each token to be manipulated in order to map an element of data (e.g. raining or not) through variations in orientation. This manipulation was essential to make it possible to map variations in the data to each token. It was also necessary in order to undo mappings, so that the system could be reused with different data- sets, thereby supporting the aim of constructive visualisation (i.e. different users mapping different data with the system). To support the ability for associations and manipulations on individual tokens, it was important through the iterations of design to consider these details of how individual tokens interrelated, in addition to decisions about overall shape.

The concept of constraints also informed the design of the interactions through the ways in which the tokens were constrained. The use of racks as a typical example of linear constraint mapping was influential in designing the system to represent the rain data. The absolute position of each token within a rack was a representation of a particular day within the month and the relative position of each rack of tokens was an indication of months within the year. Within the hard border of the MDF frame, the tight-fitting alignment of each row of tokens to its neighbour formed a series of constraints within the racks of tokens. These constraints were less rigid, than that provided by the hard border of the frame, because the elastic cord meant that individual tokens could be eased out of formation and rotated to a new orientation. There was a degree of ‘play’ necessary within this constraint in order to support these manipulations.

In the final system, for mapping energy use, a third type of physical constraint was employed. I used magnetic force to secure the position of each token. Interestingly, unlike most of the physical constraints that often have visual cues that can suggest how to interact with them, magnetic force creates a type of invisible physical constraint. Visual indications, such as the engravings of placement positions on the removeable racks and the visible magnets on the faces of the tokens, were employed to cue the users to the location of the otherwise invisible physical magnetic constraints. Each token had a cubic form, which like the hexagon cubes also supported tessellation and thus also allowed the mapping and formation of racks. Because these connections were made through magnetic

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rather than mechanical couplings, tokens could be arranged, in the X, Y and Z axes. These magnetic couplings also gave the tokens a slightly ‘lively’ quality where they would seem to want to connect when close together. Overall, this quality made the process of connecting tokens quicker, but sometimes, a token would ‘refuse’ to be added in a specific orientation, because of the polarity of its magnets. The property of polarity of the magnets was not consciously employed in the designs presented here but could provide another form of constraint to work with in similar systems.

The physical board for the final system included seven removable racks to represent seven energy consuming categories, which played an analogous role to the elastic strings of tokens in the rain data system. Each rack represents specific energy consuming category and has a unique colour assigned to it. To insert and remove each rack to and from the physical board was supported by designing the physical constraint; each rack could slide in and out of one of the unoccupied channels on the physical board and the hard borders on the physical board physically and visually guided these interactions. This provided an ability to nest and un-nest constraints within the system at several levels: tokens within a day; days within a category; and categories within the board. This physical constraint support to move and relocate the tokens for each category provides an opportunity for participants to reflect and interact with each category in isolation from the rest.

4.6 CHAPTER SUMMARY

The final design for collaborative data physicalisation based on a token and constraint system supports constructive visualisation, the quality that means a user can, using the same physical tokens, build a physical visualisation and reassemble and rebuild it. To address this quality, I designed the cubic physical tokens with an embedded magnet that could snap to other tokens in any of six possible directions in three dimensions. The tokens can also associate with the physical board through physical constraints. With the constructive approach, users should be able to rearrange, modify and occasionally move their constructed physicalisation. This design decision was the result of the iterations that considered the design of physical tokens and constraints.

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In this study, I used iterations of design and reflection as part of the research process. At the micro level I reflected on each decision made to design the physical token and constraint system, and at the macro level, I reflected on decisions informed by theories and concepts highlighted in the literature review chapter. However, this was not sufficient to finalise the system for collaborative data physicalisation. I should also highlight designing the activity for the context of use was as much of a challenge as designing of the data physicalisation itself. I went through various stages and reflected on each to design the final session to test the system with household members. I brainstormed the idea personally and in a group context. Through iterations, I also familiarised myself and reflected on the data physicalisation experience for the context of use by using Lego bricks. This iterative and reflective design process informed the design of the system and the entire final activity for the household members. This design process also informed the design of the physical tokens, the physical board, the tip-cards and the final activity session.

To design a collaborative data physicalisation system requires equal attention to both its collaborative and physicalisation aspects. I started focused on the physical side of the system with the aim to design a general approach toward physicalisation, and then I moved to the collaborative side of the study. I paid close attention to the design of the physical and geometrical form of each unit for the data physicalisation. Next, I moved on to learning, testing and reflecting on how to map the data and information using the physical unit to create a physicalisation. Finally, I familiarised myself with the context of use, potential users and designed the activity for the end user.

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Chapter 5: Results and Analysis

This chapter presents the results and analysis of participant interactions and engagements in the study settings. After the chapter’s introduction (section 5.1), section 5.2 provides an overview of the design evaluation activity sessions and formation of meta-qualities as a result of grouping the identified themes based on observations and analysing the collected data from each session. Section 5.3 presents each meta-quality and explains why each particular aspect is highlighted supported by examples from collected data. A summary of the chapter is provided in (section 5.4).

5.1 INTRODUCTION

This chapter presents the results and analysis of participant interactions and engagements in the study settings. These observations describe the kind of interactions in which participants engaged when conducting the energy-mapping activity using the system for physicalisation designed and made in this project. In each session, participants used the physicalisation to reflect on and communicate about their mapping. Through these observations, I identify the themes relevant to their collaborative data physicalisation with the aim to answer the research question: RQ3. How do people employ data physicalisation as a part of their collaborative sense-making and meaning-making?

To analyse the data, I started with identifying the observed interaction from the audio-visual recordings and grouping the themes through a bottom-up and inductive process. Alongside this I also worked deductively by identifying characteristics from the literature review in a top-down process to connect them with the identified themes. (Table 13) shows the connection between meta- qualities as a result of grouping the identified themes and characteristics identified from the literature review.

In the next section, I will present and discuss meta-qualities which are the result of these processes of analysis. The discussion of each meta-quality explains why each specific aspect has been highlighted and also explains how each meta-

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quality is related to and benefits from the characteristics to support collaborative physicalisation identified in the literature review chapter. Examples of collected data in the form of screenshots of relevant interactions alongside transcribed speech are provided to support the meta-qualities and make clear the contributions of the observed interactions and behaviours. In the transcribed texts round parenthesises ( ) are used to describe an action and square brackets [ ] are used to mark part of the conversation.

Table 13. Connections between meta-qualities and identified characteristics;

Left column: meta-qualities. Top row: Identified characteristics from the literature review

AccessPoint

SharedAccess

Externalisation

Fluid Fluid Transition

FragmentedVisibility

-

Simple Interaction Simple

Direct ManipulationDirect

SimultaneousActions

InterpersonalInteraction

Non

Flexible UserArrangement In Touch with Data; Direct X X Interaction with Data

Real-World Perspective and X X X X X Access

Playful Physical Interaction X X X with Tokens

Physical System and X X X X X X X X Collaboration

Physical System and X X X X Communication

Physical System to Support X X Externalisation

Physicalisation and X X Enjoyment

5.2 OVERVIEW OF THE DESIGN EVALUATION ACTIVITY SESSIONS

To test the final system with end-users to create a collaborative data physicalisation in the context of energy consumption at the household level, I conducted the data physicalisation activity with five different households in the

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greater Brisbane area. Each session took from 70 to 80 minutes. I spent 10 to 15 minutes to set up, collect the informed consent forms and deliver a brief explanation of the activity. Each session had three parts. Part one and part two took around 30 minutes each, and the conclusion took between five and 10 minutes.

I started the session by asking the household members to suggest where we should sit and set up the scene for our activity. Out of the five study sessions, three were set up on the dining table, and two were set up on a coffee table. After setting up the camera to record the activity, I collected the informed consent forms from household members, and I confirmed with them what would be recorded by pointing at and showing the camera’s attached screen and confirming the area to be recorded before I started the recording. The camera was mounted on a tripod, and it was set up to record from a top perspective, mostly focused on the physical board and interactions around the board, tokens and tip-cards. There was no camera operator, and the whole session was recorded from the fixed perspective. This was intentional to avoid any interruption during the session and try to concentrate on the activity and physicalisation tasks.

The activity began by explaining and introducing the physical board and tokens. I demonstrated how the tokens could be used to map the data to the board according to the day and the category of energy-consuming activity. After setting up, collecting the informed consent forms and delivering a brief explanation, each session included two main parts: part one; to map the energy-consuming activities to the physical board using physical coloured magnetic tokens. Part two; introduced the tip-cards and reflecting on and relating to the mapped energy on the board. In each session, two researchers visited the household, and two household members participated.

Overall out of the five sessions, all of the participants were able to complete the activity and reported that they found it an enjoyable and engaging process. Although the goal of these design evaluation sessions was not to test the useability of the system in detail or measure the accuracy of participants mapped data, all the participants commented on how the sessions flowed naturally and the novelty of the activity and how they can see it would be useful with other data

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sets or tasks such as money management or team building activities. In this study, these design evaluation sessions helped to observe How do people employ data physicalisation as a part of their collaborative sense-making and meaning-making? And the findings led to the formation of the meta-qualities.

Evaluating the Success of the Sessions Before I begin a detailed analysis of the interactions from participants while they used the data physicalisation system it is important first to answer the question; were the sessions a success? That is: from the point of view of allowing participants to catalogue and reflect on their daily energy-consuming activities, were participants able to do this successfully?

Overwhelmingly, participants reported that the activities of making the data physicalisation and going through the tip-cards were complementary and that the sessions would not have been as effective without the physicalisation. Participants also commented on how the physicalisation activity helped them to see and identify patterns in their energy consuming activities. The tip-cards then provided the opportunity to reflect on them. One participant explained that she would be unlikely to take the same cards without having done the physicalisation activity. She continued to explain that after this experience, and because of the activity, she felt there was a much higher chance that members of her household would follow up on applying the tips that they chose more regularly. Another participant also commented that without the physicalisation and the physicalisation activity, they would not have been as receptive to the tips.

On different occasions, participants commented on how beneficial the tip- cards could be in the future and how they might use them. For instance, participants commented on a tip under the lighting category which proposed establishing lighting zones. They explained that they had realised how they used lighting in their household and that this could be improved by this tip.

When asked if running the activity with a weekly timescale was appropriate (compared to focusing on just a day), participants highlighted, that for the physicalisation to be representative at the household level it was necessary to look at the activities over a week. They gave the example of specific energy

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consuming activities such as laundry or ironing that do not happen every day and they agreed that the weekly scale was the right scale.

Participants highlighted several of the more physical aspects of the activity and the physicalisation system as positives. They commented that the tokens were colourful, and the system was fun to use. Several participants from different sessions compared the system with board games which implies an enjoyable game-like quality. One participant described the activity as a game where players build little towers, which represent different energy consuming activities. During a different session, another participant explained the system has some similarities to Scrabble but instead of having letters on each token there are different colours, and each colour is associated with a different energy consuming category. This participant also compared the system with the Monopoly board game; where players buy properties and keep the associated cards.

Participants also highlighted physical aspects of the system including the instant physical feedback and how this helped with better understanding of the data physicalisation system and mapping the energy consuming activities. For instance, P8 explained during the activity that it did not feel like he was giving his data or information away. He explained that at the same time he was mapping the data, he was also able to see the result on the board. He felt that he was learning something at the same time. He continued to explain that he was giving some data and information, and at the same time he was taking something back by learning about his energy consumption. Moreover, he continued; by grabbing a token and giving it a value, “I felt more engaged” and at the same time “more confident about what I was saying” and mapping. He highlighted that the physicality of the mapping action gave him a feeling of responsibility about what he was saying or mapping on the board.

A number of participants from different sessions described physicalisation as a new form of representation that helped them to remember their energy consuming activities for a week and reflect on them. They highlighted how helpful it was to go through the activity and remember the energy consuming activities under each category. Some of the participants highlighted that they knew a few of the tips already, but that this knowledge had not really made an impression on

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them until they went through the data physicalisation activity and mapping process. Two of the participants also commented that initially they thought it would be challenging to remember the whole week and their energy consumption at their household. One participant, in particular, highlighted that although it was just the last week, at the beginning of the activity it seemed quite complicated to remember the details. He explained that this was because energy consuming activities are such daily routines and we do not pay attention to them. He went on to explain that often we are not fully aware of what we are doing, we are doing things mechanically. However, the data physicalisation activity breaks down the mapping to smaller and more manageable tasks and because they were grouped under different categories, this made the remembering and mapping task more feasible.

Participants also commented on how the physicalisation represented how they have used the energy and how the experience and understanding of the activity can work hand in hand with the energy bill. In this regard, one participant explained that an energy bill shows how your household ranks according to others and if you compare with the previous bills, you can see if your household is consuming more or less, but you would not know how and why. He claimed the physicalisation helped him to see how they used energy under each category. Another participant (P5) from different session also commented; when you get your energy bill, you can see how much energy you have been consumed at your household, a specific kilowatt and if you went up or down. However, it is really hard to keep track of it. In comparison, he described the data physicalisation activity as more transparent and easier to understand, and also more specific on the days of the week and energy consuming activities of each category.

Other participants commented on how they could see the trends and patterns in their mapping. One explained that the activity helped them to identify where they used energy under each category and showed the trends. He made the example that heating and cooling was mostly similar during the week whereas on standby power that went “off the chart” on certain days. He also proposed that by being able to look at these trends alongside the tip-cards supported the idea of improving energy consumption by providing simple little

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steps that they could use at their household. Two other participants each from different sessions also highlighted different patterns between working days and weekends.

The feedback from the participants presented in this section support the interpretation that the sessions were a success in terms of allowing participants to capture, map and reflect on their energy consuming activities over a week. Even though formally assessing the usability of the activity was not the aim of this study, it is clear that participants viewed the activity favourably compared to other energy consumption representations they had seen and reported that they found it beneficial. Next, I will present the formation of the meta-qualities in detail.

Forming the Meta-Qualities The overarching relationships among themes were categorised into meta- qualities, which are higher-level themes that are not initially obvious and require revisiting the collected data and earlier themes multiple times to deepen observations and generate a more in-depth understanding of, and engagement with, the physical system. In addition, meta-qualities also reflect on identified theories, in this case theories such as boundary object, democratisation and externalisation as well as characteristics to support collaborative physicalisation systems.

To present observed interactions and themes can be confusing and wordy since on each occasion and during each interaction, there was more than just one interesting action to be observed. Also, the connection between the identified characteristics and themes was very close and sometimes overlapped. To clarify the complexity involved in the project’s approach, I will present one example of interactions relevant to this study and the research questions, detailing some of the possible interesting actions and interactions to be observed and to demonstrate how multiple kinds of interactions were happening simultaneously. The keywords will be highlighted with underlined text.

Example of Interactions In Session 1 (Figure 37.a) when P2 mapped the data to the board by adding tokens, P1 made sure the tokens were stuck correctly to the board. In this first

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example, both P1 and P2 manipulated the tokens directly. Non-fragmented visibility and shared access provided the chance for P1 to observe P2’s action and assist her with the task at hand. In this scenario, the simple interaction was to rotate the token and access point, support P1 to get his hand on the object of interest. Also, P1 collaborated with P2 when he worked sequentially in a tightly coupled action with P2. In this example, both researchers R1 and R2 observed and followed the process of mapping and interactions smoothly because of the non-fragmented visibility and shared access. R1 and P1 realised at the same time that some of the tokens mapped by P2 were not stuck to the board, but R1 did not manipulate the tokens directly and just made a verbal comment. Following R1’s comment, P2 demonstrated the rotation of tokens while observing P1’s Interactions (Figure 36).

1. P2 mapped the first row of yellow tokens under the ‘lighting’ category.

2. Some of the tokens did not snap to the board because of magnetic resistance between the same poles.

3. P1 manipulated the tokens mapped on the board to change the magnetic pole.

4. At the same time as P1’s action R1 commented that sometimes they need to rotate the tokens to change the magnetic pole.

5. P2 had two tokens in her left hand; she separated one of the tokens with her right hand and showed to P1 while rotating the token in her right hand to demonstrate the rotation.

R1: Yeah sometimes you need to rotate the token if they do not … because it is the wrong pole.

P2-Top-Right: Okay.

R1: Just rotate the token if it is not sticking to the….

P2-Top-Right: Rotate this way.

P1-Top-Left: (Correcting P2’s mapping)

Figure 36. Complex example

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Because of this complexity, when I present meta-qualities, I will focus on the highlighted aspects and more relevant characteristics that have stronger ties to each quality. To clarify this further; for instance, flexible user arrangement was another identified characteristic to support collaborative physicalisation. User arrangement (Figure 37) was almost always important and influential on participants’ actions and interactions as well as the researchers’ observations and the camera’s recording angle.

Figure 37. User arrangement during each session. From left to right: a. Session 1, b. Session 2, c. Session 3, d. Session 4, and e. Session 5

I will use Figure 37 for each example to provide a reference for the user arrangement during each session, but I will unpack this characteristic only when there are stronger connections to the presented quality.

5.3 OBSERVATION, ANALYSIS AND REFLECTION ON META- QUALITIES

The literature review chapter identified direct manipulation and non- fragmented visibility as two characteristics to support collaborative physicalisation. I identified these two characteristics as the most fundamental characteristics of data physicalisation. Direct manipulation of the physical object provides instant feedback, attracts users sense of touch and offers sensory joy and playfulness. Visibility or non-fragmented visibility is the inherent property of spatiality characteristics that physical interfaces offer. As physical interfaces exist in the real-physical-world, they take up physical space, and as a result, they render non-fragmented visibility. Direct manipulation and non-fragmented visibility overshadow all other characteristics and qualities recognised in the literature review (Table 4). Direct manipulation can be referred to as the strong link between action and effect, and non-fragmented visibility supports participants pointing and showing and collaborators following their gestures flawlessly to create a connection where seeing is understood as being seen. These

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two characteristics make interacting in the physical world observable and comprehensible and as a result improve awareness, communication and collaboration.

In Touch with Data; Direct Interaction with Data In physical systems designed for collaborative physicalisations, direct manipulation of data is an inherent quality, where physical interactions and actions lead to an instant understanding, as well as an interpretation as a result of the response by the system. In this regard, the system’s behaviour intently follows the principles of direct manipulation expressed in Shneiderman’s work (Shneiderman, 1983).

Often participants interacted either with the tokens or the board directly to demonstrate and explain what they were talking about or to ask a question—for example, what energy-consuming activity, what category or what day. From time to time, participants interacted with tokens or the board directly by pointing at or showing to explain or to answer a question. Direct manipulation is one of the characteristics highlighted in the literature review chapter to support collaborative physicalisation systems. The other fundamental characteristic highlighted to support collaborative data physicalisation, as shown in the literature review, is non-fragmented visibility. Non-fragmented visibility is essential for the actor to see and interact with the system as well as for the action to be seen by others. Direct interaction with data benefits from the combination of these two qualities, direct manipulation and non-fragmented visibility.

In the next section, I will present some examples of how participants employed data physicalisation as a part of their collaborative sense-making and meaning-making process using direct interaction with data.

Direct Interaction; To Demonstrate In session 2 (Figure 37.b), P3 demonstrated his approach for mapping through direct interaction; P3 moved his hand across the categories on the board, showing and explaining his mapping. He explained he would like to map the tokens according to the energy-consuming activities he remembered and moved from one category to another without following any fixed order (Figure 38).

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P3-Left: Look, I just… you know, I do not want to follow any order …

Figure 38. Session 2, Direct interaction to demonstrate

In session 4 (Figure 37.d), P8 Interacted with the tokens directly by arranging them in his hand in the exact same way they were mapped to the board and named them to make sure he understood what had been mapped to the board and if he needed to make changes to that mapping. He held tokens in one hand, pointed at the tokens and named them one by one with the other hand. When he was naming each token in his hand, he was externalising, black tokens in his hand were not just black tokens (Figure 39).

P8-Left: That is right, so we have the TV.

P8-Left: … the router.

P8-Left: … what was the other one? TV, router…

P7-Top: Chargers?

R1-Right: Yeah it was the computer.

Figure 39. Session 4, Direct interaction to demonstrate

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While R1, P7 and P8 continued talking about energy-consuming activities under ‘standby power’, P8 directly interacted with the tokens again to externalise what he was talking about. P8 had two black tokens in his hand as he was talking about energy-consuming activities under the ‘standby power’ category. He showed the tokens in his hand and indicated that, “There’s always two, the router and TV …”. He moved his hands while holding the tokens to emphasise and externalise what he was talking about (Figure 40).

R1-Right: So, the third one was the charger…

P7-Top: Or the computer

R1-Right: Okay.

P8-Left: So, it has been either that yeah or…

R1-Right: It was not a case that both of them were plugged in at the same time, same day? Do you remember?

P8-Left: It could have happened, but I could not P8tell-Left: you whichThere’s day. always two the router and TV. R1-Right: Okay, no that is fine, yeah. And that was the same thing over the last weekend and last Friday?

P7-Top: Probably Saturday only one…

P8-Left: … router and TV are always… so we can have them.

Figure 40. Session 4, Direct interaction to demonstrate

Direct Interaction; To Remember In session 2 (Figure 37.b), when P3 was trying to remember when they had used the air-conditioner over the past week, he looked at the board and said, “Friday night”, “Friday here”, while pointing at the Friday space under the ‘heating and cooling’ category. P3 used the direct interaction to point out the right

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space for mapping to his partner, and P4 mapped an orange token under ‘heating and cooling’ on Friday to the board accordingly (Figure 41).

P3-Top-Left: Friday night, we definitely turned it on Friday night. So, that was … Friday here.

Figure 41. Session 2, Direct interaction to remember

Direct Interaction; To Highlight In session 4 (Figure 37.d), P8 pointed at the ‘heating and cooling’ category, demonstrating that he identified the space on the board for the category he was about to start his mapping. Next, he went ahead and mapped the tokens on the row with the orange colour for ‘heating and cooling’ category (Figure 42).

P8-Left: Thursday … so we have used a lot of heating, cooling …

P7-Top: Fan.

P8-Left: A fan.

P7-Top: At night, in the morning …

Figure 42. Session 4, Direct interaction to highlight

In session 2 (Figure 37.b) P3 and P4 were thinking about when they had charged their laptops over the last week. P4 said last night, P3 asked her to map a token to the board and assisted by noting the current day, Wednesday, and pointing at the Wednesday column on the board. For Tuesday, he pointed at the Tuesday column first and then at the Tuesday space under the ‘standby power’ category, and his partner (P4) mapped a black token to the board accordingly (Figure 43).

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P3-Top-Left: I was thinking the laptop, you know, it requires to be charged every now and then.

P4-Left: I think I charged last night.

P3-Top-Left: You charged it last night? For sure, so put a little one (token) …

P3&P4: Last night … Wednesday, Tuesday, here, yeah.

Figure 43. Session 2, Direct interaction to highlight

In session 5 (Figure 37.e), P9 did something similar when she pointed at the red token tower for Tuesday (the day of the activity) under the ‘in the kitchen’ category and then asked P10 to map an extra token for that day (Figure 44).

P9-Top-Right: Maybe just do one more for today.

P10-Top-Left: (Mapping)

Figure 44. Session 5, Direct interaction to highlight

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Direct Interaction; To Review In session 4 (Figure 37.d), P7 was thinking out loud to remember if there were any other energy-consuming activities to be mapped to the board. She pointed at tokens mapped to the board under the ‘in the kitchen’ category, touched the top token mapped on Monday and dragged her finger on all the top tokens mapped in that category from Wednesday to Sunday. She did something similar for the ‘laundry’ and ‘hot water’ categories. They were fewer tokens mapped under the ‘laundry’ category and, nothing under the ‘hot water’ category. However, when P7 was thinking about ‘hot water’ consumption, she similarly moved her finger from Monday to Sunday over the board along the ‘hot water’ category, even though it featured no tokens. This was interesting because it seemed like the last review or check to finalise a decision or provide an answer and P7 demonstrated this through direct interaction with the system (Figure 45).

P7-Top: So, kitchen …

P7-Top: … [laundry] …

P7-Top: … [hot water] …

Figure 45. Session 4, Direct interaction to review

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Direct Interaction; To Ask a Question In session 1 (Figure 37.a), P2 asked a question; she showed the blue tokens in her hand and asked about mapping the blue tokens under the ‘hot water’ category (Figure 46).

P2-Top-Right: So, hot water is like, because I bath, I use, and I bath him [toddler] as well. So, considered once or like twice?

Figure 46. Session 1, Direct interaction to ask a question

Direct Interaction; To Explain In session 3 (Figure 37.c), R1 asked a question. He pointed at the tallest tower under the ‘in the kitchen’ category and asked, “do you remember what makes this day so tall?” (Figure 47).

R1-Top: Do you remember what makes this day so tall? What was it … that you did in the kitchen?

P3-top-Left: Tuesday.

P3-Top-Left: Yes, because we used the microwave, which we do not use every single day.

P4-Bottom-Left: Yeah but it was the fridge. The fridge is connected every day.

P3-Top-Left: Yeah, well that makes the line but …

P3-top-Left: … he is asking why Tuesday was particularly high.

P4-Bottom-Left: Oh, okay.

R1-Top: It was the grill, right?

P3-Top-Left: It was the grill, and what else? And the Nutribullet, whatever it is.

Figure 47. Session 3, Direct interaction to explain

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P3 answered the question and explained to P4 what made the tower of tokens so tall on that day under the “in the kitchen’ category. As P3 was pointing at the tower, he moved his finger vertically along the tower to demonstrate and highlight the difference (Figure 47). Initially, P4 answered the tower was tall because of the fridge, but P3 explained, “The fridge is connected every day”, and used direct interaction to demonstrate by moving his finger horizontally across the board along the ‘in the kitchen’ category.

Direct Interaction; To Relate and Reasoning In session 2 (Figure 37.b), P3 asked P4’s opinion regarding their overall energy consumption. P4 had a different opinion initially; she pointed at the board and tokens and said to P3, “…the energy consumption is pretty much the same every day …”. P3 also pointed at the board and tokens, and explained that it was not the same: “but [it] is not, look at that …” He then highlighted the tallest tower under ‘in the kitchen’ category and said, “but it is clear, according to this data we put together in here, that Tuesday that we spend more energy than [on] others. And it is interesting” (Figure 48).

P3-Top-Left: … what do you say?

P4-Bottom-Left: Yes. Sorry, I was not listening, I got lost. But you did something, something about you realise that one day you will consume more than the others, but for me, I found like, for me the energy consumption is pretty much the same every day …

P3-Top-Left: But [it] is not, look at that …

P4-Bottom-Left: Apart from the laundry. Yeah, but I would say with common practice you use the phone every day, you charge the phones every day, you use hot water.

P3-Top-Left: I mean I am not judging, you know, I am not being harsh on ourselves or anything like that, but it is clear, according to this data we put together in here, that Tuesday that we spend more energy than [on] others. And it is interesting.

Figure 48. Session 2, Direct interaction to relate and reasoning

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Direct Interaction; To Assist In session 4 (Figure 37.d), as P7 was about to map her energy-consuming activity under the ‘laundry’ category, she asked, “where is it?”. Her partner (P8) pointed at the space on the board under the ‘laundry’ category for that day to assist P7 with her mapping (Figure 49).

P8-Top-Left: … In the laundry area, nothing today?

P7-Top: Yeah, I did iron. Where is it?

P8-Top-Left: (Pointing to the right spot on the board)

R1-Right-Top: (Pointing at the green colour tokens for Laundry)

Figure 49. Session 4, Direct interaction to assist

From the observations presented above, it is clear that direct manipulation is almost always coupled with non-fragmented visibility. But occasionally shared access and access points were also essential characteristics for participants to get their hands on the object of interest. Shared access and access points are two other characteristics highlighted in the literature review chapter to support collaborative physicalisation systems. Direct interaction with the data benefits from all four identified characteristics: direct manipulation, non-fragmented visibility, shared access and access points. As a part of the meaning-making process and sense-making process participants sometimes pointed at tokens, the board or tip-cards to guide others or attract their attention. At times, they used the tokens, the board and/or the tip-cards to support their argument or referred to them to support their reasoning. Participants also used direct manipulation to mimic and imitate their actions by using the remaining tokens off the board to show how they had performed their mapping or to explain their understanding of the whole activity.

Real-World Perspective and Access Physicalisation provides a physical and visual appearance for raw data, and as a result visibility or non-fragmented visibility become the natural properties of the spatial characteristics that physicalisations offer. As they exist in the real-

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physical-world, they take up real space, and as a result, they create non- fragmented visibility. Observers, audiences and participants can choose their preferred perspective to observe and interact with data. This is a quality of real- world perspective. Real-world perspective gains directly from non-fragmented visibility, shared-access and access-points, characteristics highlighted in the literature review chapter as key to support collaborative physicalisation systems. As highlighted earlier, shared access is the link between non-fragmented visibility and access points. In other words, shared access explains that all participants should be able to see what is going on as the action happens and be able to get their hand on the object(s) of interest. In the next section, I will present some examples of how participants employed data physicalisation using real- world perspective as a part of their collaborative sense-making and meaning- making process.

Real-World Perspective and Access; To Support Collaboration In session 3 (Figure 37.c), P5 and P6 were working together; P6 was mapping under ‘standby power’ and P5 was quietly observing his mapping. It seems P5 thought ahead and realised that P6 would need more black tokens soon to continue his mapping. Real-world perspective and access provided non- fragmented visibility and access for P5. As a result, he was able to reach his hand to the other side of the table and move a bulk of black tokens to the other side, close to P6. In this scenario, real-world perspective made it possible for P5 to pay close attention and collaborate with P5 by providing more tokens for his mapping (Figure 50).

P5-Top-Left: … the PlayStation … we play PlayStation every night pretty much. That is, right, isn’t it? (Laughs)

P6-Top-Right: (Is about to continue mapping)

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P6-Top-Right: So, it will be one cube for the PlayStation one cube for the TV?

P5-Top-Left: (Provides more black tokens from another side of the table for P6)

R1-Bottom-Right: Yes.

Figure 50. Session 3, Real-World perspective and access to support collaboration by providing

In a similar manner, later in session 3 (Figure 37.c), P6 was mapping under the ‘Standby power’ category, and he needed one more token to finish his mapping. P5 had the last token of the right colour in his hand. P5 mapped the token on behalf of P6 and said, “Here it is”. In this case, real-world perspective made it possible for others to observe P6’s interaction and for P5 to get his hand to the board and help P6 with the task on hand and complete his mapping (Figure 51).

P5-Top-Left: What about your other PlayStation, did you use that?

P6-Top-Right: Only got plugged in on Sunday.

R1-Bottom-Right: Maybe you drop one token for that?

P5-Top-Left: Here it is.

Figure 51. Session 3, Real-World perspective and access to support collaboration by complete mapping

Real-World Perspective and Access; To Remind In session 2 (Figure 37.b), P3 was thinking out loud and explaining the mapping he was about to do; he said: “So, fans. We have got two fans, so I am going to put two”. Real-world perspective made it possible for R1 and P4 to remind him and comment on the existing mapping on the board. P3 was about to add two orange tokens for fans under the ‘heating and cooling’ category. R1 pointed directly at the board and reminded P3 of the first row of orange tokens on the board representing one fan. P4 also commented and confirmed that she had mapped one fan to the board earlier (Figure 52).

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P3-Top-Left: So, fans. We have got two fans, so I am going to put two …

R1-Top-Right: [Your partner] already did one.

P4-Bottom-Left: Yeah, that is the first thing we did ...

Figure 52. Session 2, Real-World perspective and access, to remind

Real-World Perspective and Access; To Support Fluid Transitions In session 2 (Figure 37.b), P3 was about to map under the ‘standby power’ category; he needed some black tokens to start mapping. He had two black tokens in his hands, and it seemed he had located the nearest source of black tokens. After he mapped the tokens in his hand, he took a few black tokens from his partner’s hand and continued his mapping; later he continued mapping by taking more tokens from the table where the board was set up. P3 was externalising by talking to his partner, explaining what each token represented and mapping at the same time. Real-world perspective assisted P3 to identify the nearest source of tokens for his mapping —he started with the tokens in his hand and continued taking the correct colour tokens from his partner’s hand and later smoothly changed to use the tokens from the table (Figure 53).

P3-Top-Left: I am going to do something similar with the computer because it is… you leave, you know, too many tabs open and I cannot turn it off, so it is always on. Always, every day.

Figure 53. Session 2, Real-World perspective and access to support fluid transitions

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Real-World Perspective and Access; To Support Paying Attention In session 3 (Figure 37.c), P5 and P6 were working together by mapping the fridge under the ‘in the kitchen’ category, with both participants mapping simultaneously (Figure 54). P5 asked about the extractor fan, and after having a brief conversation with R2 and R1 continued to map the extractor fan under the ‘in the kitchen’ category, adding one red token to each day. He asked and confirmed if his housemate (P6) was using the fan during the week. When he finished his mapping, P6 added one red token on Friday under ‘in the kitchen’ category and said: “So, we do not put one on the Friday for the fridge” the token represented the fridge on Friday. Real-world perspective made it possible for participants to access the tokens and the board when it was needed. In this collaborative example, P6 followed P5’s mapping action sequentially and mapped his token to the board. R1 asked participants if there was anything else to add, and P5 thought quickly and added another red token on Sunday and said: “Oh, I did use [it] on Sunday the sandwich press. And that is it”. P6 laughed. It seemed that both participants had fun thinking about and remembering their energy- consuming activities over the past week and mapping them to the board (Figure 55).

R2: And what about like the fridge, I assume you have got a fridge or…?

P6-Top-Right: Yeah

P5-Top-Left: You mean like the usage of the fridge?

R2: Yeah pretty much just on the whole time.

R1-Bottom-Right: Yeah no, no but the fridge is always on, right?

P5-Top-Left: It is always plugged yeah, we need the fridge always.

R1-Bottom-Right: Then, in that case, I think you should drop (map) one token for each day because the fridge is always on. R1-Bottom-Right: No, no you take one extra and put in each day even though you are P5-Top-Left: Oh, so drop one? not, you were not home, for example, you did not open the fridge. But the fridge was using electricity working. And even if you can think about anything else like that, then I would like you to consider, maybe for other categories if not for the kitchen.

P5-Top-Left and P6-Top-Right: (Mapping)

Figure 54. Session 3, Participants working together by mapping simultaneously

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P5-Top-Left: What about the fan? The extractor?

R2: Oh, the extractor fan?

P5-Top-Left: The extractor fan does that count? We always use…

R2: …yeah…

R1-Bottom-Right: …yeah, yeah …. P5-Top-Left: …so when we cook. So that counts, so Sunday did you use it on Saturday? No.

P6-Top-Right: No.

P5-Top-Left: “But I used it on Monday.

P6-Top-Right: Better put one on for…

P5-Top-Left: …I used it on Tuesday….and you used it on Wednesday the extractor fan?

P6-Top-Right: No.

P5-Top-Left: No okay.

P6-Top-Right: So, we do not put one on the Friday for the fridge.

R1-Bottom-Right: Awesome, great any other thing in the kitchen? Do you have a boiler or…?

P5-Top-Left: Yeah, we never use it.

P6-Top-Right: The kettle I have not used the kettle at all last week.

R1-Bottom-Right: Easy.

P5-Top-Left: Oh, I did use on Sunday the sandwich press. That is it.

P5-Top-Left: (Laughter) Figure 55. Session 3, Real-World perspective and access to support paying attention

P6-Top-Right: (Laughter) In this simple example and many other presented examples in this chapter; non-fragmented visibility and shared access were the reasons for the action and/or interaction to be observed, and access point was the quality that provided the chance for participants to get their hands on the object of interest.

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Real-World Perspective and Access; To Support Multi-Tasking and Work Beyond the Interface In session 1 (Figure 37.a), when participants and researchers were talking about the ‘standby power’ category, R1 and R2 were asking about different activities, and P1 and P2 were answering together while P2 was mapping the tokens to the board. In this scenario, P1 was answering R1’s questions, and he was monitoring the mapping process by his partner (P2) while holding their toddler (which was a personal task beyond the interface). It seems the real-world perspective and access made it possible for participants to transit smoothly between the tasks on hand and even tasks beyond the interface. In this scenario, P2 was answering R1’s questions while mapping the tokens to the board (Figure 56).

R1: … do you watch the TV every day or …? P2-Top-Left: Yeah, every evening just [we] have the TV on.

P1-Top-Right: While eating or cooking we will. R1: Yeah okay, okay. And how about charging your devices?

P1-Top-Left: Every day.

R1: Every day? Okay, maybe you drop another token. P2-Top-Left: Yeah.

R1: And then … R2: … what about computers?

R1: Yeah do you have a PC at home or …

P1-Top-Right: No, we do not have a PC. P2-Top-Left: No, we use laptops. P1-Top-Right: … it is all laptops.

(P1 initially was sitting on the right side of the P2, which means Top-Left of the picture, but because he was holding their toddler, he moved with the child to the other side.)

P2-Top-Left: (Mapping and answering researchers’ questions.)

P1-Top-right: (Holding the child, monitoring the mapping and answering researchers’ questions.)

Figure 56. Session 1, Real-World perspective and access to support multi-tasking and work

beyond the interface

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In session 2 (Figure 37.b), the participants and researchers were talking about different energy-consuming activities. It seemed P3 was thinking about and searching for different energy-consuming activities by looking around the room from his seating position. P3 started mapping one token per day under the ‘standby power’ category. This was while he was commenting on his partner’s response to their household’s energy-consuming behaviours and habits. Halfway through the mapping, R1 asked if tokens represented the telephone (landline). Earlier when P3 was actively involved in the conversation and answering questions, he was also searching for an energy-consuming activity to map to the board. He looked at the telephone and said it had been “overlooked”. In this case, real-world perspective and access made it possible for P3 to search beyond the interface while he was involved in the conversation. At the same time, P3 was thinking about other energy-consuming activities and paying attention to his surroundings and what was taking place during the activity session beyond the interface (Figure 57).

P3-Top-Left: … it is overlooked … Yeah, that is true. She is [P4] better than me when it comes to saving … when it comes to being, you know, discipline[d]. Like it is hard to have discipline and a habit of checking on stuff.”

R1-Top-Right: These are for the telephone, right?

P3-Top-Left: Yes … Figure 57. Session 2, Real-World perspective and access to support work beyond the interface

Real-World Perspective and Access; To Support Simple Interaction In session 1 (Figure 37.a), P2 was mapping cooking under the ‘in the kitchen’ category. She mapped two red tokens on the weekend, saying, “…. these two days definitely we cook”. She looked at the board and said, “Those two days? Friday, we cook and …”. She then moved one of the red tokens from Saturday to Friday and said, “oh sorry [that] was not last [Saturday] … it was Friday yeah” and corrected her mapping. More than one quality involved in this scenario made it possible for P2 to correct her mapping on the spot and instantly. In this scenario, real-world perspective and access provided support for qualities such as non-

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fragmented visibility and access-points, as well as simple interaction and fluid transition which made it possible for P2 to correct her mapping. P2 was mapping, thinking out loud and explaining before correcting her mapping and it seemed so easy to correct her mistake on the spot (Figure 58).

P2-Top: …. these two days definitely we cook Those two days? Friday, we cook and…

P2-Top: … oh sorry [that] was not last [Saturday] … it was Friday yeah.

Figure 58. Session 1, Real-World perspective and access to support simple interaction

Real-World Perspective and Access; To Support Fluid Transition In session 2 (Figure 2.b), P3 and P4 and R1 were talking about the tokens mapped to the board and reflecting on them. P3 and R1 were talking about ‘Standby power’, and P4 seemed to be looking at the ‘other’ category. She said, “Ah, I think I have got one”. It appears she had been thinking about this since earlier, and then she went ahead and mapped a white token to the board under the ‘other’ category and said: “I use the shaver machine” (Figure 59).

P4-Bottom-Left: Ah, I think I have got one, I did not add it there.

R1-Top-Right: You can go ahead.

P4-Bottom-Left: But sometimes I use the shaver machine …

Figure 59. Session 2, Real-World perspective and access to support the fluid transition

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In this example, real-world perspective and access assisted P4 to make a smooth transition from talking about one energy consuming category to thinking about another category and from reflecting on the mapped tokens on the board to mapping a new token on the board.

In addition to real-world perspective and access, physicalisation provides physical mass for data, affording certain other characteristics which are unique because of their physicality. The phrase affordance refers to the physical, natural and inherent properties of a thing. I also argue that the quality of physical affordances offers a sort of physical syntax through the expression with physical parts of the physicalisation. Affordances change the effort used in the working memory by externalisation and relying on our understanding and experiences from our environment. As a result, this physical quality can support some of the other characteristics identified in the literature review chapter to support collaborative physicalisation systems, such as simple interaction and fluid transition.

It is essential for participants to be able to move smoothly and efficiently between tasks during the activity. These tasks include interacting with the system, personal and group tasks, and occasionally independent work beyond the interface.

Simple interactions can support fluid transition. From the examples presented above, when participants were working together, they would sometimes shift between individual and group tasks, which is the quality that fluid transition provides. As highlighted earlier, fluid transition includes a range of interactions with the system, personal and group tasks, and occasionally independent work beyond the interface.

There are similar examples from each session when participants finished their mapping and started looking at tip-cards and reflecting on their mapped data on the board. From time to time participants decided to add new tokens to the board while reflecting on their mapped data; fluid transition quality helped them to map their token to the board and immediately follow up with their reflection on and relating to the task on hand.

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Playful Physical Interaction with Tokens The physical experience was one of the most significant aspects of this study. During the activity, participants were almost always interacting with the tokens physically and holding at least one token in their hand; they often played with the magnetic force between the tokens. It seemed that the participants were attracted to the tokens and willingly maintained a physical interaction.

In this section, I will focus on playful physical interaction. Participants interacted with the tokens on different occasions—sometimes, participants played with the magnetic force between the tokens by separating them halfway and letting the magnetic force pull them back together, which was usually followed by a snapping sound. Sometimes, participants rotated tokens in their hands or, while tokens were connected, rotated them around their magnetic axes Physical interaction with tokens in these manners occurred on different occasions, such as when participants were talking, listening or seemed to be thinking which could have been part of the interpersonal communication or teamwork and as a result of the collaboration process. This can suggest that physical interaction with the tokens was fun and helped participants to focus and concentrate better. To link back to the findings of the literature review, physical objects provide instant feedback, attract users’ sense of touch, and offer sensory joy and playfulness. Directness in direct manipulation can refer to the link between action and effect, which can be beneficial when the data is physically presented and interacted with, improving awareness and collaboration. In this section, I will present how participants employed data physicalisation as a part of their collaborative sense-making and meaning-making process and how playful physical interaction was part of this process.

Playful Physical Interaction with Tokens; When Listening In session 1 (Figure 37.a), P2 asked R1 if she should map one or two blue tokens for hot water consumption for when she took a bath with her toddler (Figure 60).

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P2-Top-Right: So, hot water is like, because [of] I bath, I use, and I bath him (the child) as well. So, considered once or like twice?

R1: Answered P2’s question. (She was playing with the magnetic force between the tokens while listening.)

Figure 60. Session 1, Playful physical interaction with tokens when listening

In this example P2 was mapping hot water consumption to the board under the ‘hot water’ category, she separated a blue token with her right hand from others in her left hand, showed it to R1 and asked her question. When R1 answered, P2 paused and listened, she was playing with the magnetic force between the tokens while listening (Figure 60).

In session 3 (Figure 37.c), P6 was playing with the magnetic force between the tokens while listening to P5’s question about how to map an energy- consuming activity under the ‘in the kitchen’ category. P5 asked if they should separate their energy consuming activity ‘in the kitchen’ category (Figure 61.

P5-Top-Left: …can we, for example, separate, for example, you [P6] don't cook in the morning, you do not use at all the kitchen in the morning, but I do use the blender for shakes, right?

P6-Top-Right: (Listening and playing with red tokens in his hand while P5 mapping.)

Figure 61. Session 3, Playful physical interaction with tokens when listening

Playful Physical Interaction with Tokens; When Listening, Answering and Demonstrating

In session 4 (Figure 37.d), R1 asked P7 and P8 whether it was easier to think about their energy-consuming activities day by day or by each category. P8 was playing with the magnetic force between the tokens in his hands as he was listening to his partner and carried on playing with the tokens when he started his answer—he held the tokens with his left hand and started moving his hand

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along the direction of the days on the board to explain his mapping approach. After giving his explanation, he continued to play with the tokens in a similar fashion, this time using both hands (Figure 62).

R1-Right: Yeah okay. So, I just have a few questions. In the beginning, we start to look at the energy consuming activity over the days and then somewhere along the way we switched to.

R1-Right: … like each category. And then we came back again to the days, was it any different for you like was it easier to remember if you would go only thinking about only hot water or only lights or was it easier to go Friday, day by day?

P8-Left: (Playing with tokens and listening to others.)

P8-Left: Yeah exactly because for me I actually felt like it was all over the place. I am normally more like I do not know…

P8-Left: …like I would go the week. Like I would make sure I finish the whole week the whole day sorry. And then I would go to the next day.

Figure 62. Session 4, Playful physical interaction with tokens when listening, answering and demonstrating

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Playful Physical Interaction with Tokens; When Thinking Out Loud and Mapping In session 1 (Figure 37.a), P2 took a few tokens from the table; she played with the magnetic force between them as she thought out loud about energy- consuming activities under the ‘laundry’ category. She said: “ Yeah, I think it would be on … definitely on [the] weekend, because we wash[ed] up twice so it should be twice” and she pointed at the two tokens in her hand and mapped them to the board accordingly (Figure 63).

P2-Top: For the … is it laundry? Laundry you did it on …

P2-Top: Yeah, I think it would be on … definitely on [the] weekend, because we wash[ed] up twice so it should be twice.

(P2 was playing with the magnetic force between tokens in her hand when she was thinking out loud.)

Figure 63. Session 1, Playful physical interaction with tokens when thinking out loud and mapping

Similarly, in session 4 (Figure 37.d), P7 was playing with the magnetic force between two white tokens in her hand and sometimes rotating the tokens in her hand along their magnetic axis while they were connected to each other. She was

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thinking out loud; she said: “the thing in the bathroom” and did not reveal the ‘thing’, but her partner said ‘no’ and then P7 remembered that she used the hairdryer on that day, so she used one of the tokens in her hand to map the data to the board. It seems she was thinking about an energy-consuming activity under the ‘other’ category while she was playing with tokens in her hand (Figure 64).

P7-Top: … Oh, there is something that I am not sure if it is always that … the thing in the bathroom? Is that always on?

P8-Top-Left: No.

P7-Top: And … and the hairdryer.

P8-Top-Left: Oh yeah.

R1-Right: Last night or yesterday?

P8-Top-Left: Yesterday

R1-Right: Okay then.

Figure 64. Session 4, Playful physical interaction with tokens when thinking out loud and mapping

Playful Physical Interaction with Tokens; When Thinking Out Loud and Demonstrating In session 5 (Figure 37.e), P9 and P10 decided to map charging their phones under the ‘other’ category, since they never left them plugged in while they were not charging. P9 had a few tokens in her hand and was playing with the magnetic force between them and rotating them in her hand when she was thinking out loud and answering P10’s questions. She pointed at the board, indicating the row for the ‘other’ category, took some white tokens, and started mapping (Figure 65).

R2: What about charging your mobile phones and that, do you do you that every … P10-Top-Left: … yes, I do not leave it plugged in though. P9-Top-Right: No, not plugged in. R2: So, you had charged it a couple of times a week? P9-Top-Right: Yes, just put it on while it is charging and …

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P1-Top-Left: Probably once a day, you would have to do it once a day.

P9-Top-Right: Yes, well it would be once [a] day.

R2: Okay.

P9-Top-Right: (Playing with tokens in her hands)

P9-Top-Right: So maybe that is not standby though, would that be other? If we just plug it in when we are charging it?

Figure 65. Session 5, Playful physical interaction with tokens when thinking out loud and demonstrating

Playful Physical Interaction with Tokens; When Thinking In session 1 (Figure 37.a) after the participants finished mapping for the ‘standby power’ category, P2 still had a few black tokens in her hands. She was answering questions and playing with the tokens; she had three black tokens stuck together in one line and was rotating them in her hands.

Meanwhile, the others were talking about what might be an energy- consuming activity under the ‘other’ category. P2 was quiet and continuously rotating the tokens in her hands. She then broke her silence and said, ‘Vacuum’. It seems that she was thinking while playing with tokens—although she was not active in the conversation for a few seconds, she remembered an energy- consuming activity for the ‘other’ category. She then put the black tokens back on the table, took one white token from the pile in front of her, and mapped vacuuming to the board according to the day and category of the activity (Figure 66).

R1: Okay and is there anything that we did not talk about in all of these categories that you can put it on [the] white colour? I do not know. Like did you do any, I just give you an example perhaps gardening or anything that uses any devices?

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P1: No

R1: Okay or any in the garage did you use any devices or …

R2: Power tools or something …

R1: Power tools.

P2-Top: (Playing with black tokens.)

P2-Top: Oh, vacuum.

P2-Top: (Mapping a white token on the

board for vacuum.)

P1: Oh, vacuum.

R2: Vacuum oh yeah that is a good one.

Figure 66. Session 1, Playful physical interaction with tokens when thinking

In general, physical interaction with tokens in a playful manner was a typical interaction during all five sessions. In session 4 (Figure 37.d) from time to time both P7 and P8 played with the magnetic force between tokens during the mapping phase as well as while reading the tip-cards. In session 5 (Figure 37.e), R1 placed a bulk of tokens on the table in front of P9 and P10 to begin the first part of the activity. Both participants immediately took a few tokens from the pile and started interacting with them physically and discovered their magnetic force. In session 2 (Figure 37.b), participants playfully interacted with the tokens throughout the whole session. For example, P4 had some tokens in her hand for the entire first part of the activity, playing and interacting with the magnetic force between tokens, rotating tokens in her hands, holding tokens, and sometimes building abstract forms on the table. The first part of the activity mainly involved

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mapping energy-consuming activities from the past week to the board using the colourful tokens, according to the day and category of the energy-consuming activity.

Physical System and Collaboration When participants work together on collaborative tasks, it is common for them to interact with designed objects and the physical system at the same time. Although participants’ actions can be interrupted during collaboration by the limitations and flexibility of the physical system and their interfaces, teamwork usually consists of different collaborative styles. Some of the common styles include working in parallel, working sequentially in tightly coupled activities, working independently, and working under assumed roles. The collaborative quality of the physical system is aligned with the simultaneous-action characteristic identified in the literature review chapter to support collaborative physicalisation systems. In this section, I will present how participants employed data physicalisation as a part of their collaborative sense-making and meaning- making process by using the physical system collaboratively.

Physical System and Collaboration; Working in Parallel In session 2 (Figure 37.b), P3 and P4 were acting simultaneously and worked in parallel while they mapped the ‘Standby power’ category. On these occasions, one participant often started from one side of the board and the other participant from the opposite side. For example, P3 started mapping from Monday and moved towards the weekend, and P4 went in the opposite direction, moving from the weekend towards the beginning of the week under the ‘Stand By Power’ category (Figure 67).

P3-Top-Left: Standby power, yeah. We charge the phones every night. Every night.

P4-Bottom-Left: Oh, yes ….

Figure 67. Session 2, Physical system and collaboration; working in parallel

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Similarly, in session 4 (Figure 37.d), after R1 reminded participants about ‘Lighting’. P7 and P8 worked in parallel and at the same time to map under the ‘Lighting’ category. P7 started mapping from his right to left and P8 started from the opposite direction (Figure 68).

R1-Right: Yeah and the thing that I took a note to get back to; it was even though you did not use hot water for taking [a] shower but did you use lights for taking [a] shower?

P7-Top-Left: Yes, we did.

R1-Right: And I guess maybe you can drop one whenever you used the bathroom P7-Top-Left: The bathroom. So not on Saturday because we do not use …

P8-Top: Well we take showers on Saturday and Sunday.

P7-Top-Left: Yeah exactly because we went, we take a shower in the morning. While we were not here in the afternoon, so we did not turn on the …

R1-Right: … lights

P7-Top-Left: … light

P8-Top: Yeah, yeah so, I am saying everything except for Saturday.

Figure 68. Session 4, Physical system and collaboration; working in parallel

Also, in session 5 (Figure 37.e), R1 and participants were talked about the R1-Right: … lights ‘Heating and Cooling’ category. P9 and P10 decided to use one token per day to represent both fan and air-conditioner. TheyP7 -workedTop-Left: in… lightparallel and mapped their tokens to the board simultaneously under P8‘H-eatingTop: Yeah, and Cooling’ yeah so, category I am saying(Figure everything except for Saturday. 69).

R1: It was so hot, if you have used additional ones [fan], then you can think about it and add one token to… [multiple voices]

P9-Top-Right: So, we will only do one on that one.

P10-Top-Left: We did not use any fans today; I did not even use the fan in my office today believe it or not

P9-Top-Right: No but I would have had the fan on (multiple voices), so we will just do one on that one and the other ones.

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P10: The weekend we used that, that is about right.

R1: So, this is representing a fan and air conditioning?

P10-Top-Left and P9-Top-right: Yes

R1: Okay cool.

Figure 69. Session 5, Physical system and collaboration; working in parallel

Physical System and Collaboration; Working in Parallel and Under Assumed Roles In session 2 (Figure 37.b), P3 and P4 were mapping together. P3 mentioned “electric toothbrush”, and there was a conversation about the toothbrush and the fact that its engine is old and must be plugged in every day. They decided to map the toothbrush under ‘Other’ category. P4 had the right colour token in her hand, and P3 pointed to the right spot on the board and asked her to map the tokens accordingly. The simultaneous action happened when P3 and P4 started working in parallel on this task. They were talking about the activity at the same time, and it seems towards the end each took an assumed role. P3 pointed at the right spot and asked for mapping and guided P4 as she mapped the tokens to the board. R1 was also involved in the conversation, confirming the idea and pointing at the right spot on the board (Figure 70). This was another good example on focusing on ‘what’ and ‘how’ aspects of energy consumption instead of ‘how much’. Since all the tokens under each category had the same colour and size it could have been a concern if the main focus was on how much energy has been used at the household. For example, if the focus was on comparing energy consumption on using electric toothbrush vs. fan and/or air-conditioner. However, in this study the relation to scales of consumption was not the main concern and participants were focused on what and how they used energy under each category.

P3-Top-Left: You know I was thinking, we have an electric toothbrush, right? And the thing is because they are… the machine, the body of the engine, is quite old, we replace the heads of course, but because [...] the body is quite old it requires to be plugged [in] 24/7 otherwise it goes off ….

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R1-Top-Right: Yeah, that is a good idea.

P3-Top-Left: Put it on.

P4-Left-Bottom: And that will be every day.

Figure 70. Session 2, Physical system and collaboration; working in parallel and under assumed roles

Physical System and Collaboration; Working in Parallel, Undo and Continue In session 2 (Figure 37.b), P3 and P4 were working together to map under the ‘Hot Water’ category. P3 started mapping while he was saying “Hot water is always connected. Always.” P4 disagreed and said, “No, not every … but I will just …”. P3 stopped and reversed his mapping by taking back the token mapped to the board. P3 and P4 discussed the task at hand further to make sure they were on the same page. Then P4 continued his mapping. Simultaneous action in this example was P3’s and P4’s conversation about mapping parallel to the act of mapping by P4 (Figure 71).

P3-Top-Left: Hot water is always connected. Always R1-Top-Right: But think about when you take a shower [for example] P4-Bottom-Left: Yeah, so it would be in the morning, at night. P3-Top-Left: But she uses her water in the morning.

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P4-Bottom-Left: No, not every… but I will just… P3-Top-Left: Did you do it on Monday? P3-Top-Left: I… I remember saying something to you because you were taking a shower with hot water at night time and it was already warm. P4-Bottom-Left: But would that concern… hot water… P3-Top-Left: Yeah but you are using it, it does not really matter. P4-Bottom-Left: To warm a little bit and then I just turn off and then up on the cold water so that count as still? R1-Top-Right: Yeah, I guess. P3-Top-Left: Yeah, you use it. P4-Bottom-Left: Ok I was [doing] it for every day. P3-Top-Left: There we go.

Figure 71. Session 2, Physical system and collaboration; working in parallel, undo and continue

Physical System and Collaboration; Working Sequentially and In Parallel In Session 1 (Figure 37.a) P2 mapped the data to the board by adding tokens, P1 made sure the tokens were stuck correctly to the board. P1 worked sequentially in a tightly coupled action with P2. R1 and P1 realised at the same time that some of the tokens mapped by P2 were not stuck to the board, but R1 did not touch the tokens and just made a verbal comment. Following R1’s comment, P2 demonstrated the rotation of tokens while observing P1’s interactions. In this scenario R1’s verbal comment, P2’s demonstration, and P1’s correction the mapping happened in parallel action and simultaneously (Figure 72).

P2-Top-Right: (Mapping the tokens to the board.)

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R1: Yeah sometimes you need to rotate the token if they do not because it is the wrong pole.

P2-Top-Right: Okay

P1-Top-Left: (Corrects the mapping)

R1: Just rotate the token if it is not sticking to the…

P2-Top-Right: Rotate this way Figure 72. Session 1, Physical system and collaboration; working sequentially and in parallel

Physical System and Collaboration; Working Sequentially In session 2 (Figure 37.b), working together and collaboration occurred when P3 and P4 were mapping under the ‘Standby Power’ category. First P3 mapped his token to the board, and in a tightly coupled action, P4 mapped her token. This was when participants talked and thought out loud while mapping. P3 pointed at the board and together with P4 did their mapping. On this occasion the simultaneous action and teamwork were in a sequential form; one participant’s mapping action was in sequence with the other (Figure 73).

P3-Top-Left: I was thinking the laptop, you know, it requires to be charged every now and then.

P4-Bottom-Left: I think I charged last night.

P3-Top-Left: You charged it last night? For sure, so put a little one …

P3-top-Left & P4-Bottom-Left: Last night … Wednesday, Tuesday, yeah.

P4-Bottom-Left: Yeah, I think it is once. Like one day between yeah, because we are doing last week so yeah that should be fine.

P3-Top-Left: I remember I did on Friday as well. No, no, not Friday, Saturday.

P4-Bottom-Left: That is Saturday, here.

Figure 73. Session 2, Physical system and collaboration; working sequentially

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Physical System and Collaboration; Working Independently In session 4 (Figure 37.d), P7 and P8 were mapping simultaneously but under a different category. The different category also meant a different part of the board as each category has a specific row on the board. P7 was mapping the fans under the ‘Heating and Cooling’ category while P8 worked independently and mapped under ‘In the Kitchen’ for cooking dinner. Both participants were talking about their mapping and thought out loud about what was happening. Usually, when this was the case, participants communicated verbally and informed others what was happening. Often participants remembered certain patterns of their habits or behaviours about their energy consumption. For example, if one participant took showers every morning, or in this household’s case, when participants realised that they had used fans every day, P7 mapped the tokens to the board for an entire week, using one token per fan per day. Meanwhile, P8 focused on other energy-consuming activities under different categories, and different days, in this case, cooking dinner was placed under the ‘In the Kitchen’ category for the day of the activity (Figure 74).

P8-Top-Left: Heating/cooling.

P7-Top: Yeah that one. The fans.

P8-Top-Left: Fans everywhere.

R1-Top-Right: … that means two fans only? You do not have a third one?

P8-Top-Left: Well actually yes so that would be, it would be three yes. Because of that one…

P8-Top-Left: …later for dinner.

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P7-Top: (Continued mapping three fans for the remaining of the days during the week.)

Figure 74. Session 4, Physical system and collaboration; working independently

In this section, I have presented only examples in which the physical system was at the core of the collaboration. There were other forms of collaboration during each session which were the result of the whole session experience and participant involvement during the activity and not only because of the physical systems, for example, collaboration occurred when one participant reminded another to remember a certain action. For instance, in session 4 (Figure 37.d), P7 reminded P8 by saying, “We did not have bread, so we do not use the toaster” on Friday. When the physical system was at the core of the collaboration, in addition to the presented qualities such as real-world perspective and access, the flexible user arrangement characteristic was also important. When participants were sitting next to each other facing the board, the participant on the left had easier access to the left side of the board, and the participant on the right had easier access to the right side of the board. Flexible user arrangement was one of the identified characteristics in the literature review chapter to support collaborative physicalisation systems. Considering the physical arrangement of the system and participants, it is no exaggeration to say user arrangement can, to a degree, inform the type of collaboration and how participants choose to collaborate together.

Physical System and Communication To promote group activities requires considering the communication and interpersonal interaction at the core of collaboration. This can provide the primary mechanism that people use to interact collaboratively. Interpersonal interaction is one of the characteristics identified in the literature review chapter to support collaborative physicalisation systems.

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Sometimes, participants thought out loud at the same time, which seemed like a form of communication. For example, when participants were thinking out loud and mapping at the same time, they were working together, helping and correcting each other, but were also individually contributing to the mapping. In this section, I will present how participants employed data physicalisation as a part of their collaborative sense-making and meaning-making process by using the physical system to communicate.

Physical System and Communication; Direct Interaction In session 2 (Figure 37.b), P3 was trying to remember when they had used the air-conditioner in their household over the past week. He was looking at the board and thinking out loud. He said, “Friday night” and he said “Friday, here”, pointing directly at the Friday space on the board under the ‘Heating and Cooling’ category and showing it to his partner (P4). Then P4 added an orange token under the ‘Heating and Cooling’ category for Friday. Communication in this example was achieved through pointing and showing, and when one participant thought aloud and asked for another’s help with mapping. Usually, in similar cases, a participant who asked for help assisted another participant by pointing out the correct spot on the board according to the category and day of the energy- consuming activity (Figure 75).

P3-Top-Left: Friday night, we definitely turned it on Friday night, so that was … Friday here.

P4-Bottom-Left: (Mapping accordingly)

Figure 75. Session 2, Physical system and communication; direct interaction

Later in session 2 (Figure 37.b), P3 and P4 were mapping energy consumption for charging the laptop under the ‘Standby Power’ category. P4 remembered she had charged their laptop the night before. Both participants were thinking out loud: “[Today is] Wednesday [and last night was] Tuesday”. P3 pointed at the Tuesday spot on the board with his index finger; he had a few black

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tokens in his other hand, but he waited until his partner (P4) mapped a black token on Tuesday under the ‘Standby Power’ category. P4 also had another token in her hand ready to map under ‘Standby Power’, but she did not. She hovered her hand over the ‘Standby Power’ category on the board holding the black, but she did not map. She said, “Yeah, I think it is once”. Then P3 continued, “I remember I did it [charging the laptop] on Friday as well”, and he used one of the black tokens in his hand to map on Friday under ‘Standby Power’, but quickly changed his mind, saying, “No, no, not Friday, Saturday”. Then he mapped the token on Thursday instead of Saturday by mistake. Both R1 and P4 pointed at Saturday and P3 corrected his mapping. Often when participants mapped together or thought out loud and mapped, they communicated. All the participants usually followed the mapping process closely and paid attention; they commented and became involved by pointing and showing and, when necessary, asked others to correct their mapping. This example indicates how direct interaction with the system beyond characteristics such as non- fragmented visibility and shared access assist with communication (Figure 76).

P3-Top-Left: I was thinking the laptop, you know, it requires to be charged every now and then.

P4-Bottom-Left: I think I charged last night.

P3-Top-Left: You charged it last night? For sure, so put a little one …

P3-Top-Left & P4-Bottom-Left: Last night … Wednesday, Tuesday, yeah.

P4-Bottom-Left: Yeah, I think it is once. Like one day between yeah, because we are doing last week so yeah that should be fine.

P3-Top-Left: I remember I did on Friday as well. No, no, not Friday, Saturday.

P4-Bottom-Left: That is Saturday, here.

Figure 76. Session 2, Physical system andP3 c-ommunication;Top-Left: There d onirect Saturday, interaction yes.

Physical System and Communication; Externalisation In session 3 (Figure 37.c), P6 mapped a red token under the ‘In the Kitchen’ category on Friday and explained to everyone that it represented fridge energy

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consumption on Friday. P6 communicated by externalising his action and thought and provided an explanation while mapping to inform others (Figure 77).

P6-Top-Right: So, we did not put one on the Friday for the fridge.

Figure 77. Session 3, Physical system and communication; externalisation

Later in session 3 (Figure 37.c), P6 was mapping under the ‘Lighting’ category, and P5 asked about his mapping. P6 explained what the recently mapped tokens on the board represented. P6 mapped two yellow tokens under the ‘Lighting’ category for Friday and Saturday on the board. He explained that they represented lighting when he was watching TV in his room on Friday and Saturday. In this example, P6 communicated through externalisation to answer P5’s question and explained his action (Figure 78).

P5-Top-Left: What was that for?

P6-Top-Right: Another Friday night and Saturday night watching TV in my room [and I] had the light on

Figure 78. Session 3, Physical system and communication; externalisation

Data physicalisation is a concern with the physical representation of data. As humans, we generate and share externalisations of our thoughts to assist our cognition by supplying shared references directly or indirectly. Cognitive psychologists use the term ‘external representation’ as a result of the interpretation of occurrence in the external physical world; these interpretations consist of spatial connections and can be differentiated from the internal cognitive model. Externalisation was one of the characteristics identified in the

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literature review chapter to support collaborative physicalisation systems. Externalisation supports communication, and it can facilitate creating a meaningful mental image. In the next section, I will focus on the externalisation quality and how participants employed data physicalisation as a part of their collaborative sense-making and meaning-making process.

Physical System to Support Externalisation The notion of externalisation refers to the interrelation of physical objects’ representation and how users comprehend them. Thus, to support collaborative data physicalisation, an interface should support users to think and interact via or with the physical objects, to use the objects to support their act or interaction and support the group focus and conversation. This can also be beneficial when a design aims to support communication, negotiation and shared understanding.

In this section, I will present observed interactions in which externalisation was readily apparent in each example. Each token is a 3D-printed cubic physical unit (16 x 16 x 16 mm) with six magnetic centres, one on each side (Figure 27), which means that the tokens were very similar visually and physically—the only visual difference was their colour. Each colour represented a different category: orange for Heating and Cooling, red for In the Kitchen, blue for Hot Water, yellow for Lighting, black for Standby Power, green for Laundry, and white for Other (Table 12). This categorisation helped participants to focus on one group of activities and one colour at a time when they needed to track or discuss what was mapped on the board or what it needed to be mapped.

One exciting aspect of the activity was when participants or researchers used tokens for externalisation. The tokens mapped to the board were not physically labelled but, occasionally, participants addressed tokens based on what they represented. On these occasions, each token was not just a colourful physical unit but a representation of data, much like the data-ink concept in traditional data visualisation. Also, externalisation was observable when tokens become a tool for asking or answering a question. Tokens were externalised to understand and explain the mapping and became a tool to assist in thinking out loud and communication. I argue that, think out loud is a form of externalisation itself. Thinking out loud took place when participants spoke as they acted and

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spoke their thoughts. By doing this, they seemed to be verbalising their thought process and trying to explain their behaviour. When participants thought out loud, they were not usually talking to one person and considered everyone present their audience. In every session, participants thought aloud as they mapped their tokens to the board.

Most of the examples in this section were chosen from session four because of the camera angle, which made it easier to capture the screenshots from the video and represent the observed action using still images.

Physical System to Support Externalisation; To Ask a Question In session 1 (Figure 37.a), P2 asked a question about ‘Hot Water’. She separated one blue token with her right hand from a few others in her left hand, showed the others and asked her question, addressing the separated blue token as ‘Hot Water’. At that moment the blue token in her right hand was different from any other blue tokens. That particular blue token in P2’s hand was an externalised manifestation of ‘Hot Water’ (Figure 79).

P2-Top-Right: So, Hot-Water is like, because of I bath, I use, and I bath him (the child) as well. So, considered once or like twice?

Figure 79. Session 1, Physical system to support externalisation; to ask a question

In session 4 (Figure 37.d), Both P7 and P8 were talking about energy- consuming activities under the ‘Laundry’ category. P7 remembered that she ironed earlier that day and said, “yeah, I did iron [today]” and “Where is it?” She then pointed at green token mapped on the board under the ‘Laundry’ category for that day and said, “And yeah and today I did this”. In this example, the green token was an externalised representation of ironing and not just a green token on the board (Figure 80).

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P8-Top-Left: … In the laundry area, nothing today?

P7-Top: Yeah, I did iron. Where is it?

R1-Top-Right: But today you did … that was yesterday you did your shirt?

P7-Top: And yeah and today I did this.

R1-Top-Right: Okay.

Figure 80. Session 4, Physical system to support externalisation; to ask a question

Physical System to Support Externalisation ; To Communicate

In another occasion in session 4 ( Figure 37.d), P7, P8, and R1 had a conversation about the amplifier and whether should it be mapped under the ‘Standby Power’ or ‘Other’ category. P7 added a black token for the amplifier under ‘Standby Power’, and P8 and R1 started questioning. Since the amplifier was not constantly connected, they decided to move it to the ‘Other’ category and map a white token to the board accordingly to represent the amplifier on the days it had been used. R1 reminded participants that since they moved the amplifier to the ‘Other’ category, they had to remove the black tokens originally used to represent it in the ‘Standby Power’ category. At the time of this conversation, four black tokens were mapped on the board under ‘Standby Power’ for the days that amplifier had been used. P8 had four black tokens in his hand, connected in the same way as they were mapped on the board. He took out one token and started externalising by naming the three others, saying, “That is right, so we have the TV, the router, what was the other one?”; his partner helped him with the third, saying “charger”, and P8 then remembered the computer charger. After this exercise, P8 made it clear to P7 and R1 how they would remove a black token when there were four black tokens mapped to the board under the ‘Standby Power’ category, and what each remaining one would represent—the amplifier was out, and the three others were router, TV and computer charger. P8 was pointing at black tokens in his hand; one at the time when he was naming different energy-consuming activities under ‘Standby Power’ (Figure 81).

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R1: Okay ... But the fourth one was an amplifier which initially …

P8-Top-Left: That is right, so we have the TV…

P8-Top-Left: …the router…

P8-Top-Left: … what was the other one? TV, router…

P7-Top: Chargers?

R1: Yeah, it was the computer [charger].

Figure 81. Session 4, Physical system to support externalisation; to communicate

Physical System to Support Externalisation; To Represent Data In session 1 (Figure 37.a), P2 was showing the first row of the red tokens on the board, mapped earlier under the ‘In the Kitchen’ category to represent daily fridge energy consumption. She was pointing at them, and she said, “I can say that this is all for the fridge …” the red tokens on the board were not just red tokens, but were an externalisation of the fridge (Figure 82).

P2-Top: … so in the kitchen, wow. It is definitely every day, yeah and because [the] fridge is always on …

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P2-Top: I can say that this is all for the fridge …

Figure 82. Session 1, Physical system to support externalisation; to represent data

Physical System to Support Externalisation; To Explain Mapping In session 4 (Figure 37,d), P8 was talking about the remaining days on the board for the ‘Standby Power’ category. He was showing the two black tokens in his hand and moving his hands slightly up and down as he was talking, explaining that there should always be two energy-consuming activities (or devices) under the ‘Standby Power’ category. He said, “There’s always two, the router and TV…”, showing the two black tokens in his hand. In this example the two black tokens in P8’s hand were not just black tokens they were “the router and TV” (Figure 83).

P8-Top-Left: There’s always two, the router and TV [are] always [on] so we can have them.

Figure 83. Session 4, Physical system to support externalisation; to explain the mapping

In session 2 (Figure 37.b), P4 started mapping for the first time; she began with the TV under the ‘Standby Power’ category. She thought out loud, externalising her thoughts and explaining what the black tokens that she was adding to the board represented and why she was mapping them for each day. She said, “the TV is the only one that is on like, every day. So, it will just go on every day.” She explained that because the TV is always connected to the electricity power point on the wall, it needed to be mapped to the board on every day (Figure 84).

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P4-Bottom-Left: … the TV is the only one that is on like, every day. So, it will just go on every day.

P4-Bottom-Left: (Start mapping)

Figure 84. Session 2, Physical system to support externalisation; to explain the mapping

Physical System to Support Externalisation; To Think Out Loud and Communicate In session 4 (Figure 37,d), P7 and P8 were pointing and naming the red tokens under the ‘In the Kitchen’ category; they were thinking out loud and communicated to map the tokens together on the board. In this example, the red tokens under the ‘In the Kitchen’ category each become an externalisation for a specific energy-consuming activity ‘In the Kitchen’ (Figure 85).

P8-Top-Left: Actually, so what’s this?

R1-Top-Right: Yesterday.

P8-Top-Left: Yesterday yeah.

P7-Top: The kettle.

P8-Top-Left: The kettle then.

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P7-Top: The stove.

P8-Top-Left: And the toaster because we used it yesterday.

P7-Top: And the blender was yesterday as well.

Figure 85. Session 4, Physical system to support externalisation; to think out loud and communicate

Later in session 4 (Figure 37,d), something similar happened when P7 was about to map under the ‘In the Kitchen’ category. Again she thought out loud, trying to remember energy-consuming activities for the ‘in the kitchen’ category that had taken place at the weekend. She again named the red tokens and mapped similarly as before. She said “so what is this? The fridge?” (Figure 86).

P7-Top: And … Friday so what is this? The fridge?

R1-Right: Yeah

P7-Top: So, we used the stove…

Figure 86. Session 4, Physical system to support externalisation; to think out loud and communicate

In session 1 (Figure 37.a), P2 was looking at the board; she appeared to be checking each category and what had been mapped to it. She externalised her thinking by thinking out loud and naming each category when she looked at it. Later P1 did something similar; he looked at the board to see if he could

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remember anything else to map to it. As he looked at each day, he externalised his thinking as he thought out loud, naming every day (Figure 87).

P2-Top: … so cooling heating every day … in the kitchen … laundry… hot water …

P1-Top-Left: …Monday, Tuesday, Wednesday …

Figure 87. Session 1, Physical system to support externalisation; to think out loud and communicate

There were many examples from each session when tokens were not just the token but the externalisation of the data, they represented during the data physicalisation activity. Externalisation was also common during the sessions in a slightly different form; when the whole system was the tool for externalisation, and it was not limited to just a token. In the next section, I will present some of the examples of this kind of externalisation.

Physical System to Support Externalisation; Think Out Loud with the System In session 4 (Figure 37,d), P7 was looking at the board and thinking out loud to remember what kind of energy-consuming activity she had undertaken over the past week to map under the ‘Other’ category. She externalised her thought and mentioned: “the thing in the bathroom”, which she was not sure was always connected to the power point or not; the researchers did not ascertain what the thing was, but P7’s partner P8 said, “No”. P7 continued thinking out loud and externalising she said, “the hairdryer”; her partner responded, “Oh, yeah”, R1

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asked, “last night or yesterday?” and P7 answered “yesterday … and every morning”. She then mapped a white token for hair dryer under the ‘Other’ category for each day. In this example, it seemed that the system as a whole helped P7 to externalise her thinking and remember the different types of energy-consuming activity (Figure 88).

P7-Top: … Oh, there is something that I am not sure if it is always that … the thing in the bathroom? Is that always on?

P8-Top-Left: No.

P7-Top: And … and the hairdryer.

P8-Top-Left: Oh yeah.

R1-Right: Last night or yesterday?

P8-Top-Left: Yesterday.

R1-Right: Okay then.

P7-Top: [and] every morning.

Figure 88. Session 4, Physical system to support externalisation; think out loud with the system

Overall, the observed and presented examples of externalisation in this section had some similarities to the concept of boundary object, which entails the flexibility to adjust to specific and confined requirements. Boundary objects can carry various meanings in various environments and settings; their form and shape are ordinary so that they can be recognised across different environments. In this study tokens were more than just a token. They become a tool to assist interpersonal interactions and communication. They became an object like speaking sticks, used to attract others’ attention and allow participants to collaborate, compare or share information. On one level, each token was a data representation unit that became more meaningful during the conversation, and on another level, tokens acted as a reference to support someone’s argument, demonstrate meaning or give someone authority to talk at a meta-level.

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Physicalisation and Enjoyment Having pleasure, fun and enjoying the session resulted from participants engaging in a joyful activity or conversation, or perhaps when someone made an amusing comment that caused the others to laugh. For example, in session 3 (Figure 37.c) P5 was surprised about his housemate (P6) taking a shower with hot water, he said, “ah do you take [a] hot shower in this heat? Really?!” and made a choking sound to demonstrate what he thought that felt like, thus making everyone laugh. One could argue that P5 discovered this habit of his housemate because of this activity but, in this section, I report and reflect on the observations when participants experienced enjoyment, fun and pleasure as a result of interactions with the system or activity and how this was part of the collaborative sense-making and meaning-making process. This is similar to the concepts of “Interactive Play Objects and the Effects of Open-ended Play on Social Interaction and Fun” (Bekker et al., 2008) and also “Designing Playful Interactions for Social Interaction and Physical Play” (Bekker et al., 2010).

Physicalisation and Enjoyment; Experiencing Magnetic Force In session 1 (Figure 37.a), P2 was mapping the tokens to the board, adding a new layer of tokens on top of the existing ones. Sometimes, the tokens would not stick directly to the token below because they were situated at the wrong magnetic pole. P2 thus needed to rotate the tokens in her hand to find the right magnetic pole. When she was exploring the magnetic force and changing the sides of tokens in her hand to map them, she smiled and commented, “it is very playful” (Figure 90).

P2-Top-Right: It is very playful.

Figure 89. Session 1, Physicalisation and enjoyment; experiencing a magnetic force

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Similarly, in session 2 (Figure 37.b), when P3 was mapping the tokens to the board, they were sometimes at the opposite magnetic pole and did not stick, which required P3 to rotate the tokens to change the magnetic pole and map them correctly. When the tokens were rejecting each other, he commented, “… oops, oops ...” and wore a happy expression—it seems he was amused by how the tokens were behaving. Later in the same session, P3 mapped the tokens to the board, and they snapped into place immediately. He was pleased with his mapping and his good luck with magnetic force, mapping each token correctly to the board. He said, “it is pretty cool”, and he tried to mimic the snapping sound of the tokens: “Droop” (Figure 90).

P3-Top-Right: It is pretty cool to do this thing…

P3-Top-Right: … Droop …

Figure 90. Session 2, Physicalisation and enjoyment; experiencing a magnetic force

Physicalisation and Enjoyment; Physical Mapping and Working Together Often during the activity, and in every session with the different households, there were moments when participants appeared to be enjoying mapping their energy-consuming activity to the board individually or when they were mapping together. In session 3 (Figure 37.c), after P5 and P6 mapped most of their energy- consuming activities under the ‘In the Kitchen’ category, P5 remembered he had been using the extractor fan frequently, so he mapped red tokens to the board.

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Recalling the use of extractor fan seemed to prompt P5 to smile; presumably, he had a satisfying feeling of remembering one more activity to map to the board. Following P5’s recent mapping, P6 added a red token for Friday on the board and reminded everyone that it represented the fridge on Friday, which was the remaining part of the previous mapping before the extractor fan. P6 looked pleased about his recent mapping as well. Both participants continued talking and asking each other about other possible energy-consuming activities under the ‘In the Kitchen’ category. Towards the end of their conversation, P5 remembered that he had used a sandwich maker once over the weekend; before this, both participants were almost certain there were no more energy- consuming activities left to map for the ‘In the Kitchen’ category. Thus, when P5 remembered the sandwich maker, it seemed like an achievement, since he looked and sounded happy. Both participants laughed cheerfully, and P5 mapped a red token to the board under ‘In the Kitchen’ for Sunday (Figure 91).

P5-Top-Left: Oh, I did use on Sunday the sandwich press. That is it.

P5-Top-Left: (Laughter)

P6-Top-Right: (Laughter)

Figure 91. Session 3, Physicalisation and enjoyment; physical mapping and working together

Physicalisation and Enjoyment; The ‘other’ Category The ‘other’ category was the one that required participants to think about any additional energy-consuming activities in which they had taken part over the past week and that they chose not to map under the six identified categories on the board. The ‘Other’ category was represented by white tokens, and participants decided what to map under this category. When a participant introduced something new to map under ‘Other’, later they received a white tip- card on which to write the activity, and then contributed that card to the stack of the tip-cards for future reference.

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In session 2 (Figure 37.b), P4 remembered another energy-consuming activity under the ‘Other’ category and mapped the activity to the board by adding a white token to the applicable day. She seemed very happy and satisfied when she was mapping, saying, “… Oh yeah, I used it over the weekend so yeah”. From time to time, it looked as though mapping under the ‘Other’ category was more challenging and rewarding for participants, and they reacted with satisfaction when they mapped something under this category (Figure 92):

P4-Bottom-Left: … Oh and I thought about one as well, I have not used it, but we have got a foot spa, and it is an electrical one.

R1-Top-Right: You have not used it?

P4-Bottom-Left: No.

P3-Top-Left: No, not in the last week

P4-Bottom-Left: I used it, I think, last two weeks … Oh yeah, I used it over the

weekend so yeah. (P4 sounded very happy.)

P3-Top-Left: Did you?

P4-Bottom-Left: Yeah, I just remembered.

R1-Top-Right: Just grab a token and put it there. Figure 92. Session 2, Physicalisation and enjoyment; the ‘other’ category In session 1 (Figure 37.a), P2 was quiet for few moments after mapping under the ‘Standby Power’ category; she was interacting with the tokens in her hand playfully and then said: “Oh, vacuum”. It seemed she was thinking when she was playing with the tokens, and sounded delighted when she said: “Oh, vacuum” (Figure 93).

P2-Top-Right: Oh vacuum.

P1 P2-Top-Left: Oh vacuum.

R2: Vacuum oh yeah that is a good one.

Figure 93. Session 1, Physicalisation and enjoyment; the ‘other’ category

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In session 2 (Figure 37.b), as P4 was mapping under the ‘Heating and Cooling’ category, P3 said, “you know I was thinking …”, Indicating that he was thinking all along and trying to find something to map under the ‘Other’ category. P3 continued, “you know I was thinking, we have [an] electric toothbrush, right? And…” he sounded excited because he found something to map under ‘Other’ category (Figure 94).

P3-Top-Left: You know I was thinking; we have [an] electric toothbrush, right? And…

Figure 94. Session 2, Physicalisation and enjoyment; the ‘other’ category

Later again in session 2 (Figure 37.b), P3 and R1 were talking about ‘Standby Power’, and P4 was looking at the ‘Other’ category. P4 suddenly said, “Oh, and I thought about one as well”, suggesting that she had been thinking about this and when she remembered she sounded delighted (Figure 95)

P4-Bottom-Left: Oh, and I thought about one as well…

P4-Bottom-Left: … we have got a foot spa, and it is an electrical one.

Figure 95. Session 2, Physicalisation and enjoyment; the ‘other’ category

Overall, I collected nine white cards from the five different participatory sessions with 10 participants. This made the ‘Other’ category unique for participants to think about and map. Participants creating a new card each time they identified a new energy-consuming activity for this category represented a contribution to both the stack of the cards and the activity as a whole.

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Physicalisation and Enjoyment; Overall Feedback When I asked participants about their experience at the end of each session there was some evidence that directly indicated their overall enjoyment of the session. For example, P9 compared the activity with playing a board game. She was happy about some aspects of the activity being similar to a board game, including its physicality, colourfulness and entertaining aspect (Figure 96).

R2: So, when you described it as a board game what elements of it make you sort of say that would make you say.

P9-Top-Right: … a board game is colourful and fun to use, and this one is. I am sure it could have been you know like different colours represent different things, and it makes it … you know we could have done exactly the same thing in a much more boring way. I do not know like if everything was like if I was just doing little dots on a piece of paper or you know what I mean it is the same thing, but that just makes it more fun and …

Figure 96. Session 5, Physicalisation and enjoyment; overall feedback

5.4 SUMMARY OF META-QUALITIES

This chapter presented the meta-qualities that emerged as a result of analysing the study’s collected data. Each meta-quality was introduced and explained with relevant examples from the collected data to answer the research question and demonstrate How do people employ data physicalisation as part of their collaborative sense-making and meaning-making?

 In Touch with Data, Direct Interaction with Data

 Real-World Perspective and Access

 Playful Physical Interaction with Tokens

 Physical System and Collaboration

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 Physical System and Communication

 Physical System to Support Externalisation

 Physicalisation and Enjoyment

At the higher level to answer this fundamental question, it should be highlighted that sense-making and meaning-making is a process. Physicalisation in this study and during each session was a data-driven experience. Participants’ meaning-making process of unfolding their data by creating data physicalisation during each session involved more than visualising the data. It provided participants with a data visualisation experience and data-driven journey. In the study with the household members, participants themselves extracted the raw data from the energy-consuming activities of their everyday lives and routines and then mapped that energy use into the physical system using physical tokens.

Even though the physical board with tokens representing energy- consuming activities over a week was engaging to interact with on its own, it is a lot less meaningful compared to the whole experience and activity of building such a physicalisation. It would be incorrect to focus exclusively on the end result, the physical visualisation. The physical board and the physicalisation of energy- consuming activities over a week at the chosen household level was the result of a process of making such physical presentations. Therefore, the primary concern was and should be the process of making. This approach could be similar to both traditional and physical visualisation: the approach of learning and understanding more by doing. As a result, I have dedicated a considerable part of this thesis to the analysis of participant interactions and engagements when conducting the energy-mapping activity using the system for physicalisation designed and made in this project.

In the case of traditional, and more common, digital visualisations, non- visualisation experts mostly interact with the end-result. At a higher level of engagement, users might be aware of the data source, but almost never become involved in the process of creating a visualisation. This process is one of sense- making and meaning-making. In making a physicalisation, whether participants extracted the raw data (e.g., the study with household members and energy- consuming activities) or when the data set was provided (e.g., the rain data in

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Brisbane in 2014); participants had the opportunity to actively engage in the process of both making the physicalisation and making sense of the data themselves.

Next, I will recap each meta-quality with a brief explanation of how it contributes to the meaning-making and sense-making process.

In Touch with Data, Direct Interaction with Data In physical systems designed for collaborative physicalisations, direct manipulation of data is an inherent quality, where physical interactions and actions lead to an instant understanding as well as an interpretation as a result of the response by the system. In this regard, the system’s behaviour intently follows the principles of direct-manipulation expressed in Shneiderman’s work (Shneiderman, 1983). In this scenario, the object of interest or the object of concern is the data itself and participants have the opportunity to interact with it directly.

Real-World Perspective and Access Physicalisation provides a physical skin for abstract data, and as a result visibility or non-fragmented visibility becomes the natural property of spatial characteristics that physicalisations offer. As they exist in the real-physical- world, physicalisations take up real space, and render non-fragmented visibility. Observers, audiences, and participants can choose their preferred perspective to observe and/or interact with data. Through these interactions, participants can combine different precepts and bits of data with time to create a more vivid cognitive understanding. The meta-quality of real-world perspective and access also strongly supports the characteristics of access point and shared access which were identified earlier in the literature review chapter.

Playful Physical Interaction with Tokens The activity’s physical experience was one of the significant meta-qualities. During the activity, participants were almost always interacting with and manipulating the tokens physically and holding at least one token in their hand; they often played with the magnetic force between the tokens. It seemed that the participants were attracted to the tokens and willingly maintained a physical interaction. This meta-quality was strongly following the concept of direct

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manipulation identified in the literature review chapter in a sense that interaction and manipulating physical objects offers tactile contact and provides instant haptic feedback. The materiality of physical objects and interacting with them can be appealing to the sense of touch and provide “sensory pleasure” and “playfulness” (Hornecker & Buur, 2006, p. 440).

Physical System and Collaboration When participants work together on collaborative tasks, it is common for them to interact with designed objects, the physical system, at the same time. The limitations and flexibility of the physical system and its interfaces inform the teamwork and usually consist of different collaborative styles. The collaborative quality of the physical system is the combination of multiple characteristics identified earlier.

Physical System and Communication To promote group activities requires considering the communication and interpersonal interactions at the core of collaboration. This can provide the primary mechanism that people use to interact collaboratively. In addition to all other identified qualities and characteristics to support communication as part of the sense-making and meaning-making process, to support interpersonal interaction remained the most important quality to support communication.

Physical System to Support Externalisation The quality of multi-layered meaning and the idea of physical tokens as boundary objects was the result of the physical system acting as a tool for externalisation. On different occasions, different participants and/or researchers had their personal understandings of the tokens. Tokens had more than one role. They could convey different meanings for different participants on a personal and self-reflective level. In this study tokens were more than just a physical unit to represent data. They also facilitated the meaning-making and sense-making process. For instance, tokens become a tool to assist interpersonal interactions and also became like speaking sticks, used to attract others’ attention and allow participants to collaborate, compare or share information. On an obvious level, each token was a data representation unit that became more meaningful during

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the conversation, or that acted as a reference to support someone’s argument, demonstrate meaning, or give someone authority to talk at a meta-level.

Physicalisation and Enjoyment Having pleasure, fun and enjoying the session resulted from participants engaging in a joyful activity or conversation. This quality includes the overall excitement and feeling during the physicalisation activity; whether the physical system and direct interaction with it was the reason for pleasure or the experience and process was the reason for enjoyment.

Physicalisation and Sense-Making and Meaning-Making process Sense-making and meaning-making processes happened across a number of different levels, with an overlap of each level to the next. Participants were involved with tasks at a personal level, in a very hands-on experience with the physical material of the system and activity. The hands-on experience was connected to the meta-quality of Direct Interaction with Data. The next level of the meaning-making process was when participants reflected on the interactions and demonstrated part of their cognitive processes. These cognitive processes were directly connected to the meta-quality of Physical System to Support Externalisation. The top level of the meaning-making process was all-around collaboration and communication, which was connected to the meta-qualities of Physical System and Collaboration and Physical System and Communication.

5.5 CHAPTER SUMMARY

This chapter has presented the results of an analysis of participant interactions and engagements in the study settings. This resulted in the formation of the meta-qualities that answers the research question of How do people employ data physicalisation as a part of their collaborative sense-making and meaning- making?

This chapter presents the results and analysis of participant interactions and engagements in the study settings. These observations describe the kind of interactions in which participants engaged when conducting the energy-mapping activity using the system for physicalisation designed and made in this project. In

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each session, participants used the physicalisation to reflect on and communicate about their mapping. Through these observations, I identify the themes relevant to their collaborative data physicalisation with the aim to answer the research question: RQ3. How do people employ data physicalisation as a part of their collaborative sense-making and meaning-making?

This study was designed to assist participants to rethink and remember how, when and where they used energy over a week in their households. It was mostly up to participants to reflect on their energy consumption, but this activity of using the physicalisation system most likely made the thinking process easier and helped participants to externalise and communicate what they had done over the past week. Because of their experience with mapping the data with physical tokens to the physical board, relating to the energy efficiency messages on the tip-cards did not seem so strange or new. Participants could refer to and reflect on the fresh memory of their experience mapping their data on the board when necessary. I asked participants to choose any tip-cards they liked or to which they could relate and keep them as a reward. I believe participants chose those cards most relevant to them personally as a result of remembering their energy- consuming activities during the mapping the data to the board process and going through the tip-cards and selecting the most beneficial for them and their household. Therefore, each household chose different tip-cards for each category according to their experience of the activity and their household’s energy- consuming habits.

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Chapter 6: Discussion and Conclusion

This chapter consists of a discussion (section 6.1), which outlines the research questions in this study, the methods used to answer them, the outcomes of this thesis, and summarises the thesis findings. Section 6.2, contribution to knowledge, highlights the importance and significance of the approach towards data physicalisation in this project and how it can contribute to knowledge. Section 6.3, limitations and future work, presents the limitations and recommendations for future work, as well as a discussion of where the study may be extended, and what can be the practical implications. Finally, section 6.4 presents the conclusion.

6.1 DISCUSSION

The core of this design research project was the production of various design iterations and reflections, to design and make a collaborative physicalisation system flexible enough to map the chosen data by participants from the design evaluation sessions. The system was used and reused from one household to another. In the initial approach, I tested an early version of the system to create a physicalisation of Brisbane rain data from 2014. In the design evaluation sessions at the household level, I used the latest version of the collaborative physicalisation system with five different households in Brisbane and facilitated activity sessions in which participants mapped and physicalised their energy consumption data over one week.

Research exploring the design and use of collaborative data physicalisation systems has been mostly overlooked. This project has answered, through evaluations (chapter 5) and design insights (chapter 4) and theory (chapter 2), the following questions:

 RQ1. What concepts from collaborative tangible interaction and interface design and information design literature can be applied to support collaborative physicalisation?

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 RQ2. How can a collaborative physicalisation system be designed and made?

 RQ3. How do people employ data physicalisation as part of their collaborative sense-making and meaning-making?

The three research questions in this study, the methods used to answer them, the outcomes and contributions of this thesis are summarised in (Table 14).

Table 14. Research Questions and Answers

Research Questions Methods Collected Data/ Contribution to Outcome Research Questions

RQ1. What concepts from tangible interaction and Identified information design Literature review characteristics to Literature Survey literature can be applied to Chapter 2 support collaborative support collaborative physicalisation physicalisation?

RQ2. How can a Observation Design development collaborative Design Development and process of physicalisation system be Chapter 4 Reflective design making the system designed and made?

Observation RQ3. How do people employ data Audio-Visual Meta-Qualities as a physicalisation as part of result of analysing Design Experiment Result Chapter 5 their collaborative sense- the study’s collected making and meaning- Design Evaluation data making? Field Note

Detailed Discussion of Research Questions and Answers RQ1 What concepts from tangible interaction and information design literature can be applied to support collaborative physicalisation? To answer RQ1 in detail, the literature review within the field of collaborative tangible interaction and interface design assisted in setting the foundations for this study by recognising specific characteristics to support the design of collaborative physicalisation systems. These characteristics were

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drawing in particular from key papers and works in the field by Scott, Grant and Mandryk (2003), Ullmer, Ishii and Jacob (2005) and Hornecker and Buur (2006) introduced and discussed in the literature review chapter (Table 7). The identified characteristics are:

 Interpersonal Interaction, which was influential to promote group activities and interacting collaboratively in this project. Interpersonal interactions are based on our everyday synergy to one another and the physical world. Hindering these interactions can create interruptions in collaboration. This characteristic is directly related to the meta-qualities of Physical System and Communication as well as Physical System to Support Externalisation.

 Fluid Transition, was essential to allow users to move smoothly and efficiently between activities. These include interacting with the system, personal and group tasks, and occasionally independent work beyond the interface.

 Shared Access, facilitated group communication by providing a clear spatial connection to the system. It supported awareness and group-focus. All participants were able to see what was going on as the action took place and they were able to get their hand on the shared objects.

 Access Point, the notion of embodied constraints indicates the arrangement of the physical system. Embodied constraints such as shape, position or size of objects directly inform the activities and interactions. Being able to access and interact with these objects is at the core of the access point concept. Multiple access points supported simultaneous actions and provided an equal chance for all participants to interact with the system.

Flexible User Arrangement, there are different ways for participants to be positioned about the activity. The user arrangement influenced interpersonal interactions as well as group potential. Also, the tasks required by the activity influenced participants’ positioning. As a result, user arrangement directly impacted how users interact with the system as well as each other. These

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characteristics of Fluid Transition, Shared Access, Access Point, and Flexible User Arrangement were mostly influential in the recognition and formation of the meta-qualities of Real-World Perspective and Access as well as Physical System and Collaboration.

 Non-Fragmented Visibility, is one of the inherent characteristics that physical interfaces offer. Physical interfaces exist in the real-physical- world, they take up real space, and as a result, they render non- fragmented visibility. Non-fragmented visibility supported participants pointing and following each other’s gesture flawlessly to create a condition where seeing is understood as being seen. This promoted communication and collaboration and as a result it is connected to the meta-qualities of Physical System and Collaboration and Physical System and Communication. This characteristic is also linked directly with the meta- qualities of real-world perspective and access as well as in touch with data.

 Simultaneous Actions, when participants worked together on collaborative tasks, it is common for them to interact with the system. As highlighted in the result and analysis chapter teamwork consisted of different collaborative styles. This characteristic is part of the meta-quality of Physical System and Collaboration and as a result of positive teamwork influential in the formation of the meta-quality of physicalisation and enjoyment.

 Direct Manipulation, with the physical system supported participants to directly manipulate the objects of interest. It provided tactile feedback, haptic contact and attracted users’ sense of touch and offered playfulness and tactile joy. This characteristic is strongly impactful with the meta- qualities such as in touch with data, playful physical interaction with tokens, Physical System and Collaboration, Physical System and Communication as well as Physical System and Enjoyment.

 Simple Interaction, Simple interactions worked hand in hand with direct manipulation, and they were critical for collaboration. Simple interactions constituted small steps that allowed participants to carry on, share and

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examine their ideas instantly. This characteristic is essential for the meta- qualities of playful physical interaction and physical systems and collaboration.

 Externalisation, cognitive psychologists use the term ‘external representation’ as a result of the interpretation of occurrences in the external physical world; these interpretations consist of spatial connections and can be differentiated from the internal cognitive model. In this project, externalisation facilitated understanding, expression and communication. This characteristic is influential in the formation of the meta-quality of playful physical interaction with tokens and it was critical recognition and identifying the meta-qualities of physical system and communication and physical system to support externalisation.

Also, to identify requirements and possibilities to design and test a collaborative physicalisation system the literature survey within the field of physicalisations and data and information design assists in setting the base for more explicit understandings of physicalisation to be considered a visualisation and identify the gap within the existing physicalisation examples. To do so, the minimal set of requirements by Kosara in particular, readability and recognisability were recognised as a requirement for any kind of physicalisation (Kosara, 2007).

RQ2 How can a collaborative physicalisation system be designed and made? The second research question deals with the process of design and how designers or makers should go about designing and making such systems. The design development chapter provides a detailed account of the process of designing and making the system. This process was based on a reflective design approach. This approach started with the process of form-making and designing a system flexible enough to map different data sets. Through an iterative and reflective design process, the system was trialled with a sample data set. After getting familiar with the context of use, considering the participants and the environment and reflecting during the creative exploration sessions, the final system was designed and made to be used by household members in an informal design evaluation. Similarly, the final stage which was to design the activity with

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participants to test the system, was the result of the reflective design approach. To design the final activity session was influenced by the creation of the design experiment which allowed to do the reflection. In addition to design insights, personal observation and reflection of the researcher as a designer from each experiment informed the formation of the final activity.

The system was informally evaluated with five different groups of household members within the greater Brisbane area to map their energy consumption activities using the collaborative physicalisation system at their households.

The final design in this project for collaborative data physicalisation was based on a token and constraint concept and supported constructive visualisation. This means a user can use the same physical tokens to build a physical visualisation, reassemble and rebuild. To achieve this, I designed the cubic physical tokens with an embedded magnet that could snap to other tokens in any of six possible directions in three dimensions. The tokens can also associate with the physical board through physical constraints. With the constructive approach, users were able to rearrange, modify and occasionally move their constructed physicalisation. This design decision was the result of the iterations that considered the design of a system of physical tokens and constraints.

In this study, I used iterations of design and reflection as part of the research process. At the micro level I reflected on each decision made to design the physical token and constraint system, and at the macro level, I reflected on decisions informed by theories and concepts highlighted in the literature review chapter. To design a collaborative data physicalisation system requires equal attention to both its collaborative and physicalisation aspects. I started focused on the physical side of the system with the aim to design a general approach toward physicalisation, and then I moved to the collaborative side of the study. I paid close attention to the design of the physical and geometrical form of each unit for the data physicalisation. Next, I moved on to learning, testing and reflecting on how to map the data and information using the physical unit to create a physicalisation. Finally, I familiarised myself with the context of use, potential users and designed the activity for the end users.

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Design a System The design and making of the data physicalisation system is an iterative process. There are some fundamental questions that the designer should ask at the beginning. It is noteworthy to consider there will be limitations on designing a system for general use. This means there are always limitations on what kind of data systems can represent, what are the context of the use and who are the users.

Constructive Visualisation The quality that means a user can use the system over and over to reassemble and rebuild. Ideally, the system should be flexible enough to be used with different data sets and with minimal adjustments of the system. For instance, the system designed in this project can be used with different data sets, and the content on the tip-cards can be changed based on the context of use. Similarly, the colour on tokens and columns and rows on the board can be used to represent different variables depending on what categories make sense within the data.

System as a Toolkit Set Building data physicalisations is a time-consuming task, and also requires certain skill sets such as crafting ability, manufacturing skills, and an ability to design interactions. Therefore, designing a data physicalisation as a system or toolkit is beneficial, because it means that the effort of designing and making the physicalisation system can provide a ready-to-use set that users can apply to their own data sets.

Decide what the system can offer In order to provide a physical tool which users could represent their own data; the system needed to be flexible to present different values in a physical form. This means to decide how to map different values to physical matter. The system also needed to create a physical texture for the represented data; a united form of the physical units to represent the data that users could interact with.

Design the Interactions To design a system without considering the data set, environment, potential users and context of use can be challenging. One approach could be to consider the flexibility of the system and design the interactions that the system can offer

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and support. The process of designing the system can start with characterising the system and asking five fundamental questions;

 How?

o How will the system work? o How will it provide interactions and affordances?

 Who?

o Who are the users? o What matters to them? o What will they find appealing?

 Where?

o Where will the system be used? o What will the physical environment be like?

 What?

o What data set and information will the system be designed for?

 Why?

o Why would anyone use the system? o What is the context of use (possibilities, purpose and reason)?

Design-Make-Test-Reflect As mentioned earlier the design and making of the data physicalisation system requires an iterative process. Similar to iterative design and reflection in a user-centred design approach each iteration consists of iterative cycles of; designing, making, testing and evaluating. To design the interactions for the system the process started with designing, then the making of each physical unit followed by testing and reflecting and evaluating.

Design Practice Informed by Theory Existing theory and knowledge can be used to support the process of design and making. After identifying what is required from the system, the interaction and affordance of the system must be designed. For instance, in this project

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theories and concepts such as democratisation, constructive visualisation, affordances, boundary objects, and tokens and constraint were used.

Get to Know your Data-set To design a system suitable for data physicalisation in this project I started the design of the system without considering any specific data-set. When the design was at the stage to afford and support the basic interaction for physicalisation it was a good time to familiarise myself with a data-set better. In the case of rainfall data over Brisbane in 2014 by considering the characteristics and limitations of the system I decided to represent each day with rain or no rain for a year. This required me to understand the data set better and decide what it needed to visualise or better to say physicalise. In this example the design was at the stage that provided the right size hexagonal shape to be manipulated manually to map the minimum value of the no-rain and rain and arrange next to each other to form a physical pattern suitable for this project.

In this case, 365 physical units were needed, each to represent a day of a year. Also, each unit needed to be capable of mapping a minimum value of no rain and rain. To tie back to the theory and minimal set of requirements of data visualisation users needed to be able to read the data back from the system. In this case, users needed to be able to say if a certain day of the year was raining or not. For example, if 21 march was raining or not?

Finalising the System At this stage after finalising the design of the interaction and deciding on how a user can interact with each unit and how the units could arrange next to one another to represent no-rain and rain over a year I used another concept from existing theory to support my design.

I used the concept of the nested token similar to the design of an abacus to arrange the units next to each other in racks. Each rack indicated a month, and 12 racks next to each other represented a year. I also chose the Lego brick as a reference for a well-known physical object to be manipulated physically and manually, and I have matched the size of each unit to the size of the 4X4 standard Lego brick.

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This was the full process of designing and making a collaborative physicalisation system. Often design decisions are driven and inspired from literature review or existing theory. For instance, in this project, in addition to answering the five fundamental questions, the identified characteristics from the literature review to support collaborations were influential in making the design decisions.

The final design in this project was the result of the experience from creative design explorations. With the new objectives to design a collaborative data physicalisation for energy consumption at the household level. I have started with a similar process as explained above for the rainfall data and a data physicalisation system, however this time I knew the context of use from the beginning and the system was designed more specifically to test collaborative data physicalisation with the household members to map their energy consuming activities over a week.

RQ3 How do people employ data physicalisation as part of their collaborative sense-making and meaning-making? The third research question looks at the ways that people employ the physicalisation in use. Sense-making and meaning-making are both processes. Physicalisation in this study and during each session was a data-driven experience. Participants’ meaning-making process of unfolding their data by creating data physicalisations during each session involved more than visualising the data. It provided participants with a data visualisation experience and data- driven journey. In the study with the household members, participants themselves extracted the raw data from the energy-consuming activities of their everyday lives and routines and then mapped that energy use into the physical system using physical tokens. It would be incorrect to focus exclusively on the end result, the physical visualisation. The physical board and the physicalisation of energy-consuming activities over a week at the chosen household level was the result of a process of making such physical presentations. Therefore, the primary concern was and should be the process that users go through. This approach could be applicable to both traditional and physical visualisation: the approach of learning and understanding more by doing. However, in the case of traditional, and more common, digital visualisations, non-visualisation experts mostly

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interact with the end-result. At a higher level of engagement, users might be aware of the data source, but almost never become involved in the process of creating a visualisation. This process is one of sense-making and meaning- making. In making a physicalisation, whether participants extracted the raw data (e.g., the study with household members and energy-consuming activities) or when the data set was provided (e.g., the rain data in Brisbane in 2014). Participants had the opportunity to actively engage in the process of both making the physicalisation and making sense of the data themselves.

The outcome of the observation study and analysis of the recorded audio- visual data from design evaluation sessions led to the formation of meta-qualities. These meta-qualities indicate the characteristics that a collaborative physicalisation system should offer and how people employed physicalisation as part of their collaborative sense-making and meaning-making. These meta- qualities were introduced and fully explained in the result chapter.

To answer RQ3 I have summarised the essential characteristics of how people employed data physicalisation as a part of their collaborative sense- making and meaning-making process. Followed by explanation of each characteristic connected to the identified meta-qualities as below:

People interacted with data and the system directly and mechanically. In the physical systems designed for collaborative data physicalisations in this project, direct manipulation of data remained an inherent quality, where physical interactions and actions lead to an understanding as well as an interpretation as a result of the response by the system. In this regard, the system’s behaviour intently followed the principles of direct-manipulation expressed in Shneiderman’s work. In this scenario, the object of interest or the object of concern was the data itself, and people had the opportunity to interact with it directly. This characteristic identified as the meta quality of In Touch with Data, Direct Interaction with Data. As summarised in (Table 7) direct manipulation was one of the characteristics to support collaboration. Tangible interactions offered people physical manipulation with data, which provided tactile feedback, haptic contact and physical characteristics. These characteristics attracted people’s sense of touch and offered playfulness and tactile joy. Directness in direct

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manipulation can refer to the link between action and effect, which can be beneficial when the data is physically presented and handled. It can improve awareness and collaboration. Haptic direct manipulation is the concept recognised elsewhere in the literature review chapter to support social interaction and collaboration (Table 6). Some of the positive effects of haptic interactions include providing awareness and focus (Table 11). In touch with data and direct interaction with data remained as an inherent quality of the data physicalisation in this project where people employed data physicalisation to grab, feel and interact with physical objects in their collaborative sense-making and meaning-making process.

Through real-world perspective from, and access to the data physicalisation, data physicalisations provided a physical skin for abstract data, and as a result visibility or non-fragmented visibility becomes the natural property of spatial characteristics that physicalisations offered. As they exist in the real-physical-world, physicalisations take up real space and render non- fragmented visibility. Participants chose their preferred perspective to observe and/or interact with data. These characteristics supported participants’ pointing and collaborators following their gesture flawlessly to create a condition where seeing is understood as being seen. Interacting in the physical world is observable and comprehensible, which promotes communicative and performative tasks. Through these interactions, participants could combine different precepts and bits of data with time to create a more vivid cognitive understanding. These characteristics are related to the spatial interaction concepts recognised in the literature review chapter to support social interaction and collaboration (Table 6). The meta-quality of real-world perspective and access also strongly supports the characteristics of access point and shared access which they are related to the embodied facilitation concepts recognised in the literature review chapter to support social interaction and collaboration (Table 6). Both non-fragmented visibility and access point were among the characteristics summarised in the literature review chapter to support collaboration (Table 7). As highlighted data physicalisation is an interface with a natural spatial property, which means people employing this interface are required to move and interact in the real- physical-world. This informed the boundaries of actions and behaviours within

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the system. These structures also formed the social configuration around the system. The notion of embodied constraints indicates the arrangement of the physical system. Embodied constraints such as shape, position or size of objects in this project directly informed the activities and interactions. To access and interact with these objects is at the core of the access points concept. Multiple access points support simultaneous actions and shared access. The multiple access points concept is also supported the democratisation concept in a way that lowers the threshold and distributes the control; it provided an equal chance for all participants to interact with the system. People employed the meta quality of real-world perspective and access to see what was happening and being able to follow the visual references. For instance, when one participant used this quality to remind others about what was happening or to point out something on the board to support his/her argument. People also employed this quality to collaborate simultaneously and being able to get their hands on the objects of concern when needed in their collaborative sense-making meaning-making process. For instance, when one participant observed and realised his or her partner needed more tokens for mapping soon and he or she provided tokens without verbal conversation. Or when one participant corrected another participants mapping or followed her or his requests for mapping. Also, this quality made it possible for participants to work simultaneously and map the tokens to the board at the same time.

When people worked together on collaborative tasks, while employing the data physicalisation, it was common for them to interact with the system at the same time. The limitations and flexibility of the physical system and its interface informed the teamwork which usually consists of different collaborative styles. The styles observed aligned with those identified previously in the literature and discussed in chapter 3, including working in parallel, working sequentially in tightly coupled activities, working independently, and working under assumed roles. This quality is directly connected to the identified characteristic of Simultaneous Actions summarised in the literature review chapter (Table 7) to support collaboration. By employing the data physicalisation, people employed the physical qualities of the physicalisation to move smoothly and efficiently between activities. These activities included interacting with the system,

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personal and group tasks, and occasionally independent work beyond the interface. This quality was also directly connected to the Fluid Transition characteristic recognised in the literature review chapter to support collaboration and summarised in (Table 7).

To promote group activities requires considering the interpersonal interaction at the core of collaboration. Interpersonal interaction was among the recognised characteristics summarised in the literature review chapter to support collaboration (Table 7), which provided the primary mechanism that people used to interact collaboratively. Interpersonal interactions are based on our everyday ways of relating to one another and the physical world. It is also directly connected to spatial interaction and embodied facilitation themes to support social interaction and collaboration (Table 6). Some of the other concepts recognised in the literature review chapter to support collaboration are also supportive of communication. Concepts and characteristics such as non- fragmented visibility, shared access and flexible user arrangement (Table 7). People employed data physicalisation to support the collaborative significance of interpersonal interactions such as gesturing, deictic referencing, and meeting coordination activities in their collaborative sense-making and meaning-making process. Pointing or gesturing to the data physicalisation, or a shared object or an object of interest as part of the system facilitated group communication by providing a clear spatial connection to the object for the gesturer and also for other group members. By employing the data physicalisation, people were able to see what was going on as the action happened and were able to get their hand on the shared objects or the objects of the interest when needed. Also, as the user arrangement could influence interpersonal interactions as well as group potential. Over the sessions, the arrangement of the people around the data physicalisation system and next to one another remained flexible, and there were no fixed or pre-set arrangements required. People took different positions depending on the arrangement of their furniture at their household and where the data physicalisation system was set-up during the activity. All of these qualities are joined together to form the meta quality of physical system and communication.

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People employed the data physicalisation to support externalisation as part of their collaborative sense-making and meaning-making process. The quality of multi-layered meaning and the idea of physical tokens as boundary objects was the result of the physical system acting as a tool for externalisation. Externalisation is one of the recognised characteristics to support collaboration (Table 7) and also directly connected to the expressive representation theme to support social interaction and collaboration (Table 6). On different occasions, people had their personal understandings of the tokens. People employed tokens in more than one way. Tokens could convey different meanings for different participants on a personal and self-reflective level. In this study tokens were more than just a physical unit to represent data. They also facilitated the meaning-making and sense-making process.

For instance, people used tokens as a tool to assist interpersonal interactions and also became like speaking sticks, used to attract others’ attention and allowed people to collaborate, compare or share information. On an obvious level, each token was a data representation unit that became more meaningful during the conversation, or that acted as a reference to support someone’s argument, demonstrate meaning, or give someone authority to talk at a meta- level. The notion of externalisation refers to the interrelation of physical objects’ representations and how people comprehend them. People employed the data physicalisation to think and interact via and with the physical objects. They used the objects to support their act or interaction which supported the group focus and conversation as a part of their collaborative sense-making and meaning- making process.

The physical experience of the data physicalisation was one of the significant meta-qualities. When people employed the data physicalisation they were almost always interacting with and manipulating the tokens physically and holding at least one token in their hand; they often played with the magnetic force between the tokens. It seemed that the people were attracted to the tokens and willingly maintained a physical interaction. This quality was strongly following the concept of direct manipulation identified in the literature review chapter to support collaboration (Table 7); in a sense that interaction and manipulating physical

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object offers tactile contact and provides instant haptic feedback. While people were employing the data physicalisation the materiality of physical objects and interacting with them seemed to be appealing to the sense of touch and provided sensory pleasure and playfulness as part of their collaborative sense-making and meaning-making process.

6.2 CONTRIBUTION TO KNOWLEDGE

This section highlights the importance and significance of the approach towards physicalisation in this project and how it can contribute to knowledge.

The Uniqueness of Design for the Physicalisation As a part of designing for collaborative physicalisation in this section I would like to highlight the physicalisation aspect of the collaborative physicalisation and uniqueness of design for the physicalisation.

Shifting from a 2D screen-based to a 3D physical environment informs both the affordance and interactivity of design. In this thesis, I encourage a shift in our approaches to designing physicalisations. Considering the acceleration of processing and computing technologies, we are now living in an age in which we produce digital information every hour, minute and even second, much faster than we can process or make sense of the data. Within this vast ocean of collected data lies a significant amount of unexplored and insightful information. We must find ways to approach, explore, present, make sense of, relate to and communicate this information purposefully. I want to emphasise the word approach; only part of the suggested shift involves using technology. The other part requires a shift in the way we, as designers, think when we consider data visualisation. With the emergence of digital fabrication technologies, we have sufficient tools available to give physical shape to our visualisations. However, I argue that this is where we continue to make mistakes, in that physicalisation should be more than a physical conversion of a screen-based visualisation. As the term ‘physical visualisation’ suggests, there are two equally important elements involved in creating a physical visualisation: 1. physicality, and 2. visualisation. This is also not to imply a binary opposition between the digital and physical

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worlds. Indeed, there are potential avenues for exploring intersections between these worlds.

Designing a physical object or an artefact requires designer skills that consider how to shape forms, functionality, affordances and interactions. Moreover, how to create a visualisation requires understanding from the field of information visualisation. As a result, to explore the full potential of physicalisation requires designing a new kind of visualisation from scratch, in the real world for the real world.

This means employing all of the skills that designers have long used for designing for the physical world, but also bringing a fresh perspective drawn from transdisciplinary approach in— ‘our physical world!’—an environment that represents a promising domain for leveraging our human skills for real-world understanding and perception, manipulation and making sense of physical data visualisations. This provides new opportunities and new challenges. To ensure we make adequate use of all these opportunities, we need more empirical studies, such as this research, that consider all stakeholders and the environment.

Designing a New Kind of Visualisation In addition to understanding and skills from the field of information visualisation to understand and make-sense of data, physicalisation requires designing for affordances, interactions and embodied design as well as manufacturing and making skills. Therefore, it requires a fresh perspective and approach to visualisation in the physical domain. What can we learn and retain from screen-based visualisation? How can we avoid copying from traditional screen-based visualisations? What should be maintained and what should be changed?

One approach to address these concerns is to design a physicalisation as a system in a deep and meaningful sense, to consider users and their needs as part of the design. To consider both artistic and pragmatic qualities; things we can learn and retain from screen-based visualisations. From artistic approaches, we can learn how to make a powerful tool for communicative purposes, and from pragmatic approaches, we can learn how to support analytical tasks. We should maintain Kosara’s minimal set of requirements, in particular, readability and

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recognisability to remain a necessary requirement for any kind of visualisation. Physicalisation precedes the invention of writing. CAD applications and digital fabrication speed up the manufacturing process but manufacturing still requires time for making and some level of expertise to design, set, produce and assemble. As a designer there is a learning cycle involved in learning how to design and make use of such technologies. Physicalisation offers an externalised and physical representation that can enhance people’s understanding of data. To do so, there is a need to design a system that is flexible, easy to use and accessible to everyone, including lay users and non-experts. This connects the ideas of democratising visualisation and constructive visualisation, where the system can be used and reused by ordinary people and with different data sets to empower them to interact, communicate and make sense of data. The process of creating an adequate screen-based graphical visualisation with an acceptable result is now available to many users (Viegas et al., 2007), but we cannot claim the same for collaborative physicalisation systems. This is partially because we lack a proper conceptual model and often try to copy from existing graphical visualisations.

Data for Makers As the term physicalisation indicates physical and visualisation, with this approach, designers or makers need to familiarise themselves with both physicality and visualisation aspects. Using this approach, data are mapped to physical matter rather than pixels. This requires skills and knowledge in both data and information visualisation as well as design and fabrication techniques. The challenges in both areas are worth being considered and explored. As I highlighted earlier regarding visualisation, we should be flexible enough to design for new experiences rather than using traditional approaches based on paper and pen or screen-based visualisations and try to devise fresh ideas that consider physicality and tangibility at the core of design.

Conversely, one might raise the question that making physical objects is time-consuming and requires craftsmanship skills; however, the increased availability of CAD applications and digital fabrication is reducing the time and effort needed to manufacture physicalisations. In addition, as designers and makers, we can design and create systems in the form of tool-kits to be used and

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reused again and make them available to others. In this project the collaborative physicalisation system contained; the physical board, colourful magnetic tokens and tip-cards.

System vs Artefact As an artefact’s design could inform its functionality, the manufacturing challenge could remain the designer’s concern and not the user’s. If we design a system flexible enough to be applied to different situations, the toolkit could be used again and again, but only need to be designed and manufactured once. Following the idea from the previous section ‘Data for Makers’ supports the reasoning to design a system rather than an artefact for one-case or just one- solution; to be able to consider this type of system as an approachable method, which is almost ready to be used and does not requires much time to get the set ready. This kind of system avoids going through the entire iterations of design and making again. For example, the system used in this project can be reused in a different project with different data sets by assigning a new value to each colour and changing the content of the tip-cards, also each row and/or column can indicate different things. A physicalisation system with these characteristics can support the notion of constructive visualisation which can improve democratisation in visualisation to facilitate and empower the lay user to interact, share, make-sense and understand data. I argue that the right tools for designing and making such systems are at our disposal; CAD applications are broadly available for both educational and commercial purposes and digital manufacturing technologies are becoming more accessible. There are already reliable online services with a vast range of material and quality for prototyping as well as production worldwide.

Democratising Visualisation Through Constructive Physicalisation In this project, democratisation is defined as a system to be used as a tool by lay users to physicalise their data and create a unique and meaningful experience with that data by interacting with it. Democratising means providing a design paradigm for everyone, including non-experts in the visualisation domain, to construct physicalisations for a simple, adjustable and changeable visualisation. For data visualisation, one challenge remains regarding how to visualise Big Data

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or how to visualise larger quantities. Most of the physicalisations do not address the issue with Big Data and multi-dimensional data. However, this project adopts a different approach drawn from different perspectives to scale up the number of users and reach the broadest possible audience, making it easier for users to map their data using a physicalisation system without having much (if any) data visualisation expertise. With this approach, this project aimed to design for the physical world and a real-world platform and to be as inclusive as possible.

Finally, democratising enables the public, and not only professionals, to design, debate, publish and communicate their data and collaborate via a tool that does not require much technical expertise in coding or programming or a long learning cycle and mastery of skills such as illustrations and visualisation. To achieve this level of democratisation, this approach relies on constructive visualisation. As discussed earlier in the literature review chapter, constructive visualisation works based on our inherited understanding and background knowledge from our physical world. This level of engagement creates a unique connection between data and visualisation for anyone, including non-experts.

Through this thesis, I promote a shift in approach towards physicalisation: to design in the physical world for the physical world. I also believe we can apply our design and visualisation knowledge to designing and making such physicalisations, but we need to keep an open mind to experience and explore new methods

I identified the meta-quality of Real-World Perspective and Access, physicalisations enable users to choose their preferred perspective, and this could have a catalytic effect on understanding data similar to chemistry students using physical molecular models. The goal of physicalisation could be considered facilitating a process to form a meaningful mental image. This physical form of representation should be simple enough to be used by ordinary people, who possess less conceptual knowledge and visual literacy compared to expert users. For example, a data expert can read and understand a complex, traditional 2D graph in a book or online that might make no sense to an ordinary person.

Physicalisation can provide Real-World Access for users to explore and manipulate data first hand via Direct Interaction with Data, offering them the

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flexibility to choose the best possible angle via Real-World Perspective from which to explore, Communicate and Collaborate. They can also use the physicalisation to Externalise their cognitive process and experience the Meaning-Making and Sense-Making process with pleasure.

6.3 LIMITATIONS AND FUTURE WORK

There is still so much unknown about physicalisations, and it seems we are at a very early stage. Designing physicalisations requires expertise in both design and craftsmanship and skills in data and information visualisation. Working collaboratively in teams would be the best approach to achieve the level of expertise required; at a minimum, the team would require designers, visualisation experts, users and experts from the context of use. One way to start is to avoid complicated physicalisations, as too much information can be overwhelming and confusing to process. Throughout the process of building up complexity, it is important to maintain the democratisation of developing data physicalisations. By considering non-experts and new users’ cognitive processes and their formation of internal representations and ways of knowing – it will ensure that the physicalisations are well understood and interpreted. Over time and with more experience, study and further understanding from the field, physicalisations can become more advanced and complicated. This reasoning follows the logic of learning through a process; for example, to learn a language, one often starts with learning the alphabet, words, sentences and paragraphs before writing poetry.

The research presented here touches on each of these important aspects. However, to further progress the understanding of physicalisations requires additional empirical studies to expand the cycle of knowledge. This study was not an evaluation of how accurately or effectively people were able to recall their energy use. It was not a quantitative usability study, and the units of the tokens were unspecified, meaning that the “granularity” of the visualisations was rather coarse. It is possible that in some cases this misled participants about what were the most energy-consuming practices. For example, lots of small activities stack up more than one or two big ones.

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Future studies and research can consider different kinds of data sets, they can test the approach with different types of users, it could be a mix of different levels of expertise. Similar projects can consider transdisciplinary fields and contexts to test the idea. To take the concept of democratisation to a different level, users can be involved more with designing the system and the projects can take a participatory design approach.

Physicalisation in and for VR and AR As described earlier the aim of this research project was purely to design and create a non-digital and analogue collaborative physicalisation system. There are also many possibilities for future exploration in areas such as Virtual Reality (VR) and Augmented Reality (AR). I have a background in designing multimedia content, including interactive 3D virtual environments. Using the logic to design in the physical world for the physical world, I contend that this could be a two- way learning process, which means applying the observations, learnings, understandings and experiences from the physical world and physicalisation into digital and virtual environments.

As Gwilt nicely described, the use of expressions such as Virtual and Augmented reality has been grown more and more to explain the connection, technologies and assumptions for a diverse range of mixed digital and material (physical) forms and applications (Gwilt, 2013). He believes we can acquire properties like “networkability”, “morphology”, “replicability”, and “complexity” from the digital environment and from the material and physical world we can learn about concepts such as “value”, “heritage”, “longevity”, and “authority” (Gwilt et al., 2013, p. 2). Gwilt also supports Julier’s idea that combination of various components from data and material through design can guide to new comprehension (Julier, 2014).

With the recent improvement of virtual reality tools and technologies, there is an opportunity to consider learning from designing and creating in both physical and digital environments to inform and develop more meaningful and engaging applications in mixed and hybrid environments.

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6.4 CONCLUSION

This thesis has made a significant contribution to the understanding of collaborative physicalisation systems. As the vehicle for the contribution, a physicalisation was designed, made and tested. This thesis consists of an introduction and literature review, which includes the review of relevant work and theoretical context and background for the research topic and the methodological framework. It continues with the design research and design development process for critiquing such systems. It analyses the result of the design evaluation sessions and development of the meta-qualities. Thereby, it contributes to the understanding, clarification and process to undertake when designing a collaborative data physicalisation system. Finally, it closes with discussion and conclusion by summarising the findings, highlighting the research question and answers, discussing limitations and perspectives for future research.

The results of this thesis demonstrate that physicalisation supports communication and collaboration and can enhance the cognitive process and understanding by supporting externalisation. Some of these capabilities appear to originate from the characteristics that are exclusive to physical items, such as their inherent visual and tangible qualities. I have identified and presented some of these characteristics under meta-qualities. For instance, the meta-quality of Real-World Perspective and Access contain the flawless visual realism characteristic. Moreover, the meta-qualities of In Touch with Data and Direct Interaction with Data address the unique tangible characteristics mentioned earlier.

Although the physical approach was once the primary method of communicating data and information, there have been relatively few studies to explore the possibilities of this method in contemporary times. Perhaps our strategies for learning and communicating in contemporary times partially explain why exploring physicalisation has been overlooked. When it comes to our most common approaches towards data and information visualisations or, in a broader sense, in the current technological setting, it seems we are intensely focused on development of the technology in certain areas such as high-

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resolution and touch screens; our dilemma remains how to achieve higher and better displaying platforms with more pixels and sharper images rather than making the presentation experience richer.

This research aims to challenge the status quo and look at new visualisation paradigms. This is not underestimating the achievements of screen-based visualisations and/or technologies. My argument is that we are missing out on what can be achieved in a 3D environment, a physical and tangible world; we might need to adopt a different perspective. This means developing a set of approaches and methods beyond what we know and beyond familiar 2D screens. It seems that when we want to consider a new approach towards information design or data visualisation in a physical and tangible domain, we look for an approach similar to that we have learned over the years: designing for screen- based platforms. It can be argued that a 3D-printed bar chart or graph could function better than a digital version on screen for specific tasks, but I argue here that it is worth being open to new forms of learning and exploring. Relearning and reconsidering new ways to visualise, transfer and communicate data, information and knowledge in physical forms. The findings of this thesis demonstrate that through a consideration of the appropriate use of the meta- qualities as identified in this research, data physicalisation can engender collaboration between users, leading to a shared understanding and engagement with data.

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Appendices

6.5 APPENDIX 1, ENERGY-EFFICIENCY TIPS

Based on my testing experience with Lego bricks, observing and attaining feedback from colleagues during the internal creative exploration, using the City Smart project (‘CitySmart - Brisbane’s Sustainability Agency’, 2017), using Reduce Your Juice App (‘Reduce Your Juice on the App Store’, 2017) and studying the energy-saving webpage of the Brisbane City Council (‘Saving energy’, 2017) and energy-efficiency tips from Origin (Energy, 2017), I summarised my findings from these resources to create my own energy-efficiency tips and categorised the energy-consuming activities for the final design as follows:

Heating and Cooling

 “Insulate your home to stop air from leaking out/in. Up to 25% of heat in the home is lost through gaps.”

 “Install special purpose-built window and door seals to stop the cold/hot air from escaping.”

 “Close your curtains and shut your windows on sunny days. This will keep the sunlight from heating your home.”

 “Electronic portable heaters might be cheaper to buy, but they cost a lot to run.”

 “When you are using heaters or coolers, shut out rooms that don’t need to be warmed or cooled.”

 “When you leave the room or go to bed, turn heaters or coolers off. You can set the timer to optimise your usage.”

 “Dress for the temperature. Layering clothes and wearing wool helps keep you warm on cold days.”

 “Use electric blankets instead of heating up the entire room.”

Appendices 217

 “On hot days, when you arrive home, open the doors and windows for five minutes to let out the hot air before closing them and using your coolers.”

 “An average Australian home uses 40% of its energy on heating and cooling.”

 “The smaller the difference between indoor and outdoor temperatures, the lower the energy consumption and costs.”

 “The recommended temperature for winter is 18°C to 21°C, and for summer is 23°C to 26°C.”

 “Every degree higher heating or lower cooling is equal to a 10% increase in running costs.”

 “Using air-conditioners could cost up to 25 times more than ceiling fans.”

 “When it is too humid, run the air-conditioners for an hour to zap the moisture, then use the fan to stay comfortable.”

 “Hot air rises. On cold days, turn the ceiling fan to a lower speed to gently push the warm air back.”

 “By using a fan instead of an air-conditioner, you can save up to $400 over a summer.”

 “Position your fan near the door or window for a more sufficient cooling effect.”

 “Use inverter type air-conditioners that are sized, installed and used in accordance with manufacturer’s recommendations. Inverters use up to 40% less energy than other air-conditioners.”

In the Kitchen

 “Place your refrigerator away from direct sunlight or heat sources in a well-ventilated spot. Keep a 5 cm gap around your fridge to let the air circulate freely.”

 “If you have a second fridge, only turn it on when it is needed. A 400 L fridge can cost you around $220 a year to operate.”

218 Appendices

 “Don’t make your fridge use extra energy to cool things you are not going to use.”

 “Choose a 4+ star energy rated model when you are shopping for a new fridge.”

 “The recommended temperature for a fridge is 3°C to 5°C, and for a freezer is ‒15°C to ‒18°C.”

 “Thaw frozen food in the fridge before putting it in the microwave.”

 “Choose the microwave instead of the oven when cooking. Microwaves cook food three times faster than a standard full-size oven.”

 “Avoid opening the oven door when the oven is on. Every time you open the oven door, heat will escape and make the cooking time longer. Instead, use the oven light to check your food.”

 “When cooking, set your oven to fan forced. It cooks more quickly and evenly than the conventional setting.”

 “Use an electric kettle rather than the stove.”

 “Use your pots and pans according to the size of the burner. Using a small pot on a large burner or vice versa wastes energy.”

 “When you use the stove, keep the lids on your pots to reduce cooking time.”

 “Use the economy cycle on your dishwasher and only run it when it is full.”

 “Consider upgrading to a new dishwasher, if your current one was manufactured in the early 90s. Chances are it is using twice as much water and 40% more energy.”

Laundry

 “Use your dryer with a full load and when your clothes have been wrung well.”

 “Clothes should never be placed in the dryer when they are dripping wet.”

Appendices 219

 “Clean your dryer’s lint filter regularly.”

 “If you must use your dryer, run it on the low or medium heat.”

 “Line dry your laundry instead of using a dryer.”

 “You can save up to $115 per year by washing your clothes in cold water.”

 “Use your washing machine on an economy cycle or with the load- sensing function.”

 “Don’t underload or overload your washing machine or dryer.”

 “Use cold or warm water instead of hot water to wash your clothes and spin thoroughly before drying.”

 “A full load of washing on cold water uses a quarter of the power. This means you can save up to 75% of the power used by your washing machine.”

Lighting

 “Install motion sensors on your security lights, so you do not have to remember to turn them off at night.”

 “Separate your lighting zones so you can choose which areas to light.

 Use lamps or spotlights if you only need a small amount of light instead of main lights, and avoid halogen downlights.”

 “Lighting uses about 8% of your energy bill.”

 “Switch to LEDs or compact fluorescent light bulbs. You can save up to 80% energy per globe.”

 “Use solar lights to light up your garden pathways. They store energy during the day and then light up automatically at night.”

Hot Water

 “Switch from a standard showerhead to a 3+ star rated head. You can save your hot water up to 50%.”

 “Shorten your showers to fewer than five minutes.”

220 Appendices

 “Leave your mixer tap in the cold position so that hot water is not wasted cooling in the pipe.”

 “The recommended temperature for hot water is 50°C.”

 “Hot water uses around 25% of your home’s energy and is the largest producer of greenhouse gas emissions in the average Australian home.

 Ditch the heated towel rails. You can save up to $200 a year.”

Standby Power

 “Grab an energy-saving power-board. Up to 10% of your electricity could be consumed by gadgets and appliances that are on standby.”

 “Use standby power controllers or turn off small appliances such as your kettle or toaster when you are not using them. They might be small, but they can use up to 5% of your home’s energy.”

 “Switch your appliances off at the wall. Did you know your phone charger is still using energy even when your phone is not attached?”

 “Install a ‘free standby power controller board’. These automatically cut off the power to your electronic devices when you press the on/off button on your remote controls.”

 “The biggest energy users are the fridge, hot water system, WI-FI, plasma TV and air-conditioner.”

Some Other Online Sources www.originenergy.com.au

www.actsmart.act.gov.au

www.accombris.com.au

www.mozo.com.au

Appendices 221

6.6 APPENDIX 2, DATA MAPPED TO THE PHYSICALISATION SYSTEM

Design Evaluation Session 1

Session 1 Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Cooling/Heating

7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan

13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee 13th Coffee In The Kitchen 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 6th Kettle 5th Cooking 5th Cooking 5th Cooking 5th Cooking 4th Microwave 4th Microwave 4th Microwave 4th Microwave 4th Microwave 4th Microwave 4th Microwave 4th Fridge 4th Fridge 4th Fridge 4th Fridge 4th Fridge 4th Fridge 4th Fridge

Laundry

3rd Washing M 3rd Washing M 8th Washing M 3rd Washing M 3rd Washing M

Lighting

14th Outdoor 16th Kitchen/BR 16th Kitchen/BR 16th Kitchen/BR 16th Kitchen/BR 16th Kitchen/BR 16th Kitchen/BR 16th Kitchen/BR 16th Dining Area 16th Dining Area 16th Dining Area 16th Dining Area 16th Dining Area 16th Dining Area 16th Dining Area 1st Livingroom 1st Livingroom 1st Livingroom 1st Livingroom 1st Livingroom 1st Livingroom 1st Livingroom

Hot Water

2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower 2nd Shower

Standby

15th Wi-Fi 15th Wi-Fi 15th Wi-Fi 15th Wi-Fi 15th Wi-Fi 15th Wi-Fi 15th Wi-Fi 11th Laptop 11th Laptop 11th Laptop 11th Laptop 10th Charger 10th Charge 10th Charge 10th Charge 10th Charge 10th Charge 10th Charge 9th TV 9th TV 9th TV 9th TV 9th TV 9th TV 9th TV

Other

12th

222 Appendices

Design Evaluation Session 2

Session 2 Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Cooling/Heating

5th Fan 5th Fan 5th Fan 5th Fan 5th Fan 5th Fan 5th Fan 4th A/C 3rd Fan 3rd Fan 3rd Fan 3rd Fan 3rd Fan 3rd Fan 3rd Fan

In The Kitchen 15th Oven 14th Fridge 14th Fridge 14th Fridge 14th Fridge 14th Fridge 14th Fridge 14th Fridge 13th Microwave 12th Nutribullet 12th Nutribullet 12th Nutribullet 12th Nutribullet 12th Nutribullet 11th Gas Stove 11th Gas Stove 11th Gas Stove 11th Gas Stove 11th Gas Stove 11th Gas Stove 11th Gas Stove

Laundry

7th Washing M 7th Washing M 7th Washing M

Lighting

Kitchen Kitchen Kitchen Kitchen Kitchen Kitchen Kitchen 8th Dining Area 8th Dining Area 8th Dining Area 8th Dining Area 8th Dining Area 8th Dining Area 8th Dining Area

Hot Water

9th Shower 9th Shower 9th Shower 9th Shower 9th Shower 9th Shower 9th Shower

19th Wi-Fi 19th Wi-Fi 19th Wi-Fi 19th Wi-Fi 19th Wi-Fi 19th Wi-Fi 19th Wi-Fi Standby 18th Telephone 18th Telephone 18th Telephone 18th Telephone 18th Telephone 18th Telephone 18th Telephone 17th Mobile 17th Mobile 17th Mobile 17th Mobile 17th Mobile 17th Mobile 17th Mobile 10th Laptop 10th Laptop 2nd Computer 2nd Computer 2nd Computer 2nd Computer 2nd Computer 2nd Computer 2nd Computer 1st TV 1st TV 1st TV 1st TV 1st TV 1st TV 1st TV

Other 21st Foot Spa 20th Epilady 16th E Sander 6th E Toothbrush 6th E Toothbrush 6th E Toothbrush 6th E Toothbrush 6th E Toothbrush 6th E Toothbrush 6th E Toothbrush

Appendices 223

Design Evaluation Session 3

Session 3 Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Cooling/Heating

1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan

5th Extractor fan In The Kitchen 5th Extractor fan 4th Microwave 7th Sandwich Press 3rd Fridge 3rd Fridge 5th Extractor fan 2nd Cooking 2nd Cooking 3rd Fridge 3rd Fridge 3rd Fridge 2nd Cooking 2nd Cooking 2nd Cooking 3rd Fridge 2nd Cooking 2nd Cooking 2nd Blender 2nd Blender 2nd Blender 2nd Cooking 6th Fridge 2nd Cooking 2nd Cooking

Laundry

14th Washing M 13th Washing M 14th Washing M 15th Iron

Lighting

12th Bedroom 12th Bedroom 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Livingroom 11th Livingroom 11th Livingroom 11th Livingroom 11th Livingroom 11th Livingroom 11th Livingroom

Hot Water

10th Defrost 9th Washing 9th Washing 9th Washing 9th Washing 9th Washing 9th Washing 9th Washing th th th th th th th DWashingishes8 Shower 8 Shower 8 Shower 8 Shower 8 Shower 8 Shower 8 Shower 8th Shower 8th Shower 8th Shower 8th Shower 8th Shower 8th Shower 8th Shower

27th Wi-Fi 26th Stereo 27th Wi-Fi 27th Wi-Fi 27th Wi-Fi 22nd PlayStation 26th Stereo 26th Stereo 26th Stereo 21st earphone Standby 20th iPad 20th iPad 20th iPad 20th iPad 19th Mobile 19th Mobile 19th Mobile 27th Wi-Fi 27th Wi-Fi 27th Wi-Fi 19th Mobile 18th Mobile 18th Mobile 18th Mobile 18th Mobile 18th Mobile 18th Mobile 18th Mobile 17th TV 17th TV 17th TV 17th TV 17th TV 17th TV 17th TV 16th PlayStation 16th PlayStation 16th PlayStation 16th PlayStation 16th PlayStation 16th PlayStation 16th PlayStation

Other

23rd Vacuum 24th Toothbrush 25th E Shaver 25th E Shaver 25th E Shaver Cleaner

224 Appendices

Design Evaluation Session 4

Session 4 Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Cooling/Heating

7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 1st Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 7th Fan 1st Fan 7th Fan 7th Fan 7th Fan 7th Fan

24th Oven In The Kitchen 19th Fridge 16th Kettle 19th Fridge 19th Fridge 16th Dishwasher 17th Toaster 16th Kettle 21st Cooking 16th Toaster 17th Cooking 2nd Toaster 19th Fridge 23rd Cooking 22nd Blender 20th Microwave 16th Cooking 17th Blender 2nd Cooking 8th Cooking 19th Fridge 19th Fridge 19th Fridge

Laundry

19th Washing M 3rd Iron 3rd Iron 19th Washing M 18th Washing M

Lighting

25th Bathroom 25th Bathroom 25th Bathroom 25th Bathroom 25th Bathroom 15th Kitchen 15th Kitchen 4th Kitchen 25th Bathroom 15th Kitchen 15th Kitchen 15th Dining Area 15th Dining Area 4th Dining Area 9th Dining Area 15th Dining Area 15th Dining Area

Hot Water

Standby

14th Charger 14th Charger 5th Charger 10th Charger 14th Wi-Fi 14th Wi-Fi 5th Wi-Fi 10th Wi-Fi 14th Wi-Fi 14th Wi-Fi 14th Wi-Fi 14th TV 14th TV 5th TV 10th TV 14th TV 14th TV 14th TV

Other 27th Toothbrush 27th Toothbrush 26th Vacuum 27th Toothbrush 27th Toothbrush 27th Toothbrush 26th Vacuum 27th Toothbrush 27th Toothbrush th th th th th th th 13Cleaner Stereo 13 Stereo 11 Stereo 11 Stereo 13Cleaner Stereo 13 Stereo 13 Stereo 12th Hair Dryer 12th Hair Dryer 6th Hair Dryer 6th Hair Dryer 12th Hair Dryer 12th Hair Dryer 12th Hair Dryer

Appendices 225

Design Evaluation Session 5

Session 5 Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Cooling/Heating

2nd A/C 2nd A/C 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan 1st Fan

In The Kitchen

6th 20th Toaster 5th Cooking 5th Cooking 20th Toaster 20th Toaster 4th Dishwasher 4th Dishwasher 4th Dishwasher 4th Dishwasher 4th Dishwasher 4th Dishwasher 4th Dishwasher 3rd Fridge 3rd Fridge 3rd Fridge 3rd Fridge 3rd Fridge 3rd Fridge 3rd Fridge

Laundry

7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M 7th Washing M

Lighting

13th Bathroom 13th Bathroom 13th Bathroom 13th Bathroom 13th Bathroom 13th Bathroom 13th Bathroom 12th Bedroom 12th Bedroom 12th Bedroom 12th Bedroom 12th Bedroom 12th Bedroom 12th Bedroom 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 11th Kitchen 10th Dining Area 10th Dining Area 10th Dining Area 10th Dining Area 10th Dining Area 10th Dining Area 10th Dining Area

Hot Water

9th Shower 9th Shower 9th Shower 8th Dishes 8th Dishes 8th Dishes 8th Dishes 8th Dishes 8th Dishes 8th Dishes

Standby 18th Microwave 18th Microwave 18th Microwave 18th Microwave 18th Microwave 18th Microwave 18th Microwave 17th Wi-Fi 17th Wi-Fi 17th Wi-Fi 17th Wi-Fi 17th Wi-Fi 17th Wi-Fi 17th Wi-Fi 16th TV 16th TV 16th TV 16th TV 16th TV 16th TV 16th TV 15th Desktop P/C 15th Desktop P/C 15th Desktop P/C 15th Desktop P/C 15th Desktop P/C 15th Desktop P/C 15th Desktop P/C 14th Dyson V/C 14th Dyson V/C 14th Dyson V/C 14th Dyson V/C 14th Dyson V/C 14th Dyson V/C 14th Dyson V/C

Other 22nd Stereo 22nd Stereo 22nd Stereo 22nd Stereo 21st Epilady 22nd Stereo 22nd Stereo 22nd Stereo 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile 19th Mobile

226 Appendices

All Five Design Evaluation Sessions

MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN

Cooling/Heating

In the Kitchen the In

Laundry

Lighting

Hot water Hot

by

Stand

Other

Session 1 Session 2 Session 3 Session 4 Session 5

Appendices 227

6.7 APPENDIX 3, QUESTIONS ASKED FROM PARTICIPANTS

List of Questions Asked from Participants During Each Design Evaluation Session 1. How would you describe the artefact to others?

2. Do you feel comfortable to show the artefact to others and explain how it works?

3. Did you find artefact useful as a communicative tool?

4. Do you like to use the artefact for something else?

5. What do you like about the artefact the most?

6. What do you dislike about the artefact the most?

7. Do you think this artefact was useful?

8. Do you think this information is relevant to you?

9. Did you learn anything from the experience?

10. If you want to suggest one change what would it be?

11. Where do you store, use the artefact and why?

12. What was the effect of the artefact on your thinking about the topic?

13. What type of people do you think might like this artefact?

14. How would you like to redesign this?

15. Have you had any discussion or disagreement over the meaning or how to use the artefact?

228 Appendices