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In-Situ Sensemaking Support Systems

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In-Situ Sensemaking Support Systems In-Situ Sensemaking Support Systems Nathan Hahn CMU-HCII-20-109 September 2020 Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Aniket Kittur (Chair) Brad Myers Adam Perer Jaime Teevan Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright c 2020 Nathan Hahn This research was sponsored by the National Science Foundation (IIP-1701005, IIS-1149797), Bosch Global and Google. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: Information Systems, Human Computer Interaction, Information Seek- ing, Sensemaking Abstract In-Situ Sensemaking Support Systems The internet has become the de-facto information source for individuals — from finding a new cookie recipe, to learning how a transistor works. Tools for helping users find answers to their questions have grown tremendously in response; a user can get an answer in their search results for when the next Pirates game is, or get a list of facts about their favorite movie actress. However, for many questions, such as buying a car, there isn’t one right answer — the best choice for an individual depends on their particular set of circumstances and personal preference. For these situations, it’s up to the user to make sense of the answer space: accumulate what options are available, what the differences and features of these options are, and eventually choose between them. This process, sensemaking, is a highly iterative and cyclical process, where informa- tion is constantly being found, incorporated, restructured, summarized, and generalized. As users continue to collect new information, they need to adjust and restructure their existing information, while incorporating the key known points of the new information into their understanding of the problem. This constant adjustment of both the data and structure surrounding the data puts a significant mental burden on users, and often requires them to resort to external means to track and manage this information. In the context of online sensemaking, this can be done in notepads, tabs, word processors, spreadsheets, kanban boards, or even emails. As users proceed to move along with their data in this process, they need to manually update and transfer data between these tools, which might often be more trouble than its worth. In this work, I explore how integrated, in-situ sensemaking tools allow users to manage, structure and evaluate their sensemaking data more effectively. I posit that by reducing the interaction costs for users to externalize their mental models in four key stages of the sensemaking process: seeking, triage, structuring, and evaluation, users can more adequately juggle the large amount of information required to perform effective sensemaking. First, I explored and developed a workflow for performing sensemaking using crowdworkers with the Knowledge Accelerator System. Crowdworkers, using this workflow, were able to answer complex questions while spending less than 5 minutes on a single task, suggesting a lightweight scaffolding that could be adapted for use in individual sensemaking. This led to the creation of a sensemaking framework that connects the process of sensemaking with the cognitive processes and tools users leverage for their sensemaking tasks. Using this framework, I then went on to develop three systems for supporting individual sensemaking: Bento, Distil and Meta. These systems were designed to iii iv Abstract provide in-situ support for one or more of the four phases of seeking, triage, structuring, and evaluation. Through these tools, I was able to identify several key strategies for creating effective online sensemaking tools. These strategies suggest several promising directions for the future of integrated, in-situ browser sensemaking tools. Acknowledgments As I completed my junior year of my undergraduate degree, I would have never pictured myself completing a PhD. Luckily, I was fortunate enough to have taken my advisor, Niki Kittur’s, class on sensemaking, and the rest was history. Without his constant support and guidance, I would not be where I am now. His energy, optimism and patience have allowed me to push forward through tough times and rejection on my journey as a researcher. None of this work would have been as robust and complete without my partner in crime, Joseph Chang. He has served as a constant sounding board for my ideas, problems, frustrations, and been there throughout every single one of my research projects. His continuous friendship and support gave me necessary stability during constant uncertainty, and look forward to what we might continue to do in the future. My committee, Adam Perer, Brad Myers and Jaime Teevan, have given excellent guidance on not only this thesis work, but also many fun, novel and unique research projects. I would also like to thank my other Microsoft mentor, Shamsi Iqbal, for her continued support and guidance on not only research, but life as a researcher. I’ve had the privilege of being able to work with two wonderful UX and design professionals, Julina Coupland and Brad Breneisen, who have not only made my tools significantly easier to look at and use, but also support the work I’ve done through numerous user interviews and evaluation. My lab mates over the years, Jeff Rzeszotarski, Felicia Ng, Michael Liu, Hyeonsu Khang and Andrew Kuznetsov, have consistently provided clear feedback and a lot of help in building Ikea furniture for the lab. I’ve also had the chance to work with a large number of undergraduate RAs, whose designs and creativity inspired and refined many of the systems I’ve built. I’ve been extremely fortunate to have made many friends along the way who have helped both inspire me and ground me throughout my time at CMU. I would especially like to thank Alexandra To, Cole Gleason, Laura Licari, Michael Madaio, Rushil Khurana, Qian Yang, Joseph Seering and Judy Oden Choi for helping to make my life outside of work just as fulfilling (and filled with D&D). The aid of the wonderful staff at CMU, particularly Queenie Kravitz and Diana Rotondo, have helped to keep me on track and ensure I’m able to complete my PhD work without overburdening myself. Lastly, I would like to thank my parents, David and Nancy, and my siblings, Laurent and Stewart, who have been there for me during some of the most difficult times. Their love and support have given me those necessary breaks to de-stress, as well as the motivation to continue my work. v Contents Abstract iii Acknowledgments v Contents vii 1 Introduction 1 1.1 Online Sensemaking . .2 1.2 Modeling the Process . .4 1.3 Overview . .5 2 Background 9 2.1 Sensemaking Models . .9 2.2 Sensemaking Systems . 12 2.2.1 Seeking Tools . 13 2.2.2 Triage Tools . 13 2.2.3 Structuring Tools . 13 2.2.4 Evaluation Tools . 14 3 The Knowledge Accelerator: Distributing Sensemaking 15 3.1 The Trouble with Microtasks . 15 3.2 Related Work . 16 3.2.1 Crowdwork: Complex Cognition and Workflow . 17 3.2.2 Computational Information Synthesis . 18 3.3 System Architecture . 18 3.3.1 Inducing Structure . 18 3.3.2 Developing a Coherent Article . 21 3.4 Design Patterns . 24 3.4.1 Context before Action . 24 3.4.2 Tasks of Least Resistance: Leveraging Worker Choice . 25 3.5 Implementation . 25 3.6 Evaluation . 26 3.6.1 Method . 26 3.6.2 Results . 27 3.7 Discussion . 29 vii viii Contents 3.8 Individual Implications . 31 4 Bento: Search and Triage 33 4.1 Introduction . 34 4.1.1 Mobile Sensemaking . 35 4.2 Understanding Tabs . 36 4.3 System Design . 36 4.3.1 A Sensemaking Workspace . 37 4.3.2 Managing Sensemaking Tasks . 38 4.4 Implementation . 43 4.5 Evaluation . 43 4.5.1 Study 1 - Understanding Triage . 43 4.5.2 Study 2 - Task Management . 46 4.5.3 Study 3 - Behavioral Traces . 48 4.5.4 Summary . 50 4.6 Discussion . 51 4.7 Bento Iterations . 52 4.8 Takeaways . 53 5 Distil: Extracting and Structuring Information 55 5.1 Introduction . 56 5.2 Siphon . 57 5.2.1 Toolkit Description . 59 5.2.2 Built-In Tools . 64 5.3 Distil . 65 5.3.1 Related Work . 65 5.3.2 Clips . 67 5.3.3 Outliner . 67 5.3.4 Smart Categories . 68 5.3.5 Implementation . 71 5.4 Evaluation . 72 5.5 Results . 74 5.5.1 Category Types . 75 5.5.2 Category Evolution . 76 5.5.3 Continuous Refinement . 77 5.6 Discussion . 78 5.6.1 Limitations . 79 5.6.2 Design Suggestions . 79 5.7 Takeaways . 80 6 Meta: Extracting, Structuring and Evaluating Potential Options and Sources 83 6.1 Introduction . 83 6.2 Related Work . 85 6.2.1 Supporting Trust . 86 6.2.2 Recognizing and Extracting Options . 87 ix 6.3 System Design . 87 6.3.1 Formative Study . 87 6.3.2 Meta . 88 6.4 Evaluation . 96 6.4.1 Performance Evaluation . 96 6.4.2 Option Evaluation . 99 6.4.3 Source Trust Evaluation . 104 6.5 Discussion . 107 6.6 Takeaways . 108 7 Conclusion and Future Work 109 7.1 Strategies . 109 7.1.1 "Just in Time" Sensemaking . 109 7.1.2 Reuse Attention Signals . 110 7.1.3 Automatic Sensemaking Tasks . 111 7.1.4 Triage as First Class . 111 7.1.5 Two-way Information Flows . 112 7.1.6 Information Compression . ..
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