A Desktop/Mobile VR Game-Based Intervention To

Video Games as Deweyan Worlds: A Desktop/Mobile VR Game-based Intervention to

Improve Visuospatial Self-efficacy in Middle School Students

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

Irina Kuznetcova, MA

Graduate Program in Educational Studies

The Ohio State University

2020

Dissertation Committee

Dr. Michael Glassman, Advisor

Dr. Tzu-Jung Lin, Co-Advisor

Dr. Bryan Warnick, Committee Member

Copyrighted by

Irina Kuznetcova

2020

Abstract

Visuospatial (VS) skills, or one’s ability to mentally manipulate information about objects’ positions, relations, orientation, and transformations in space, are one of the cornerstones of STEM enrollment, retention, and achievement. Low level of visuospatial skills may deter some people from joining the STEM workforce or complicate their learning experience. In addition, gender disparities in VS performance contribute to an overall gender gap that has been an ongoing issue for many STEM disciplines. There is evidence to suggest VS skills are malleable and can be improved through training. As of

2020, however, very few comprehensive and readily available training programs exist, particularly for younger age groups and explicitly grounded in pedagogical frameworks.

The current study proposes a new direction of VS training focusing on the development of visuospatial self-efficacy, or one’s confidence that they can complete specific VS tasks. The proposed intervention is built on the intersection of three disciplines: educational psychology, educational philosophy, and game design. Video games are explored as a promising medium for the implementation of educational principles proposed by John Dewey whose philosophy along with the theory of self-efficacy

(developed by Albert Bandura) informed the development of an intervention game. The collaborative, desktop/mobile Virtual Reality game called Bond (available for download at learnspatially.com) was designed to improve visuospatial self-efficacy in middle

ii school students. A total of 169 students across 11 classrooms in 3 middle schools in a mid-western city in the United States participated in the study. The intervention took place in an elective STEM class. The participants in the experimental condition (n=96, 6 classrooms) played the intervention game during 4 sessions over the course of 2 weeks, while the participants in the control condition (n=73, 5 classrooms) engaged in typical class activities. Collected data included participants’ VS self-efficacy and performance right before and right after the intervention, grades in STEM courses at the start and the end of the school semester, and demographic information. The results revealed that the intervention significantly increased students’ VS self-efficacy but not their VS performance or grades in STEM courses. The implications and significance of the findings are discussed along with practical guidelines for designing serious games in education.

iii

Dedication

To Michael, the most incredible mentor in the world who is like a parent to me.

To Tzu-Jung, the kindest and most patient adviser ever.

To Nastya, my sister by heart.

To my mom, dad, and sister for showing their love even when we are an ocean apart.

To Shantanu, Logan, and Chris, who put me back on my feet when I lost hope.

To Gosha and Viola, who accept me the way I am through the good and bad.

To Qiannan, Ziye, and Marvin for making our research lab a happy place.

To Olga Glotova and Dmitry Vertkin, the mentors whose lessons and support I remember even years later.

And to Ann, my American mother and life mentor whose love helped me become a better version of myself.

To everyone affected by the global coronavirus crisis – we shall overcome!

Acknowledgments

This dissertation and my entire doctoral career would not have been possible without the kindness and overwhelming support of Michael Glassman, my adviser, my friend, and the most influential mentor in my life. Michael, you are the light that guided me through the best and worst of times in the past five years, and I am forever grateful for how much you have done for me. I also want to thank Tzu-Jung Lin, my co-adviser, whose kind but firm guidance made me into the scholar I am today.

Shantanu, Logan, Qiannan, Ziye, and Marvin, my research team who play-tested my game, woke up at 6 am for weeks in a row to assist me with data collection, and made me look forward to every lab meeting – you all made this thesis a reality. I thank you with all my heart for the great time we spent together.

I want to thank Nick who was my coding mentor, my best friend, and spent hours helping me with the game and supporting me through challenging times. You were the reason I felt confident to start this project in the first place and saw it through, and I will never forget that.

To Ann – thank you for giving me the emotional support that was much needed in this difficult year. You are the reason why I was able to keep going even when I felt like giving up.

Vita

2018...... M.A. Educational Psychology, The Ohio State

University

2015...... B.A. Linguistics and Intercultural Communication, The

Ohio State University

Summer 2019-Spring 2020...... Presidential Fellow, The Ohio State University

Fall 2016-Spring 2019...... Graduate Teaching Assistant, Dennis Learning Center,

The Ohio State University

Fall 2015-Summer 2016...... University Fellow, The Ohio State University

Publications

Kuznetcova, I., Glassman, M., & Lin, T. J. (2019). Multi-user virtual environments as a

pathway to distributed social networks in the classroom. Computers & Education,

130, 26-39.

Kuznetcova, I., & Glassman, M. (2018). Virtual reality: Its transformative potential. In

Benade L., Jackson M. (Eds.), Transforming education: Design, technology,

government (pp. 199-211). London: Sage.

Kuznetcova, I., Teeple, J., Glassman, M. (2018). The dialectic of the avatar: Developing

in-world identities in Second Life. Journal of Gaming & Virtual Worlds, 10(1), 59-

71.

Kuznetcova, I., Lin, T. J., Glassman, M. (2020). Teacher presence in a different light:

Authority shift in Multi-User Virtual Environments. Technology, Knowledge and

Learning. Online first.

Fields of Study

Major Field: Educational Studies

Area of Specialization: Educational Psychology

vii

Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgments...... v Vita ...... vi List of Tables ...... xi List of Figures ...... xii Chapter 1. Introduction ...... 1 Chapter 2. Literature Review ...... 5 Visuospatial skills ...... 5 The Importance of visuospatial literacy in the 21st century ...... 5 The definition and structure of VS skills ...... 7 VS skills and academic outcomes in STEM ...... 14 Gender differences in VS ability...... 16 The ‘nature’ perspective on the gender gap in VS ability ...... 17 The ‘nurture’ and ‘nature-nurture’ perspective on the gender gap in VS ability .. 18 Developing VS skills ...... 20 Summary ...... 23 Self-efficacy ...... 25 Self-efficacy as a theoretical construct ...... 25 Visuospatial self-efficacy...... 29 VSSE as a foundation for spatial interventions ...... 31 Summary ...... 33 Games and Virtual Reality as a medium for VS training ...... 35 The history of games and Virtual Reality in education ...... 35 Defining Virtual Reality and serious games ...... 35 The history of Virtual Reality and games ...... 39 Virtual Reality: the path to accessibility ...... 39 Serious games: from Oregon Trail to multiplayer online games ...... 41 VR and games in education ...... 47

viii

Games and VR in visuospatial interventions ...... 49 Summary ...... 52 The theoretical principles of designing a game-based VS intervention ...... 53 The philosophy of pragmatism ...... 53 Essential principles ...... 53 The pragmatism of Charles Pierce ...... 55 The pragmatism of William James ...... 59 The philosophy of John Dewey ...... 61 John Dewey and pragmatism ...... 61 Dewey’s philosophy of education: What counts as educative experience? ...... 63 Three pillars of educative experiences: Communication, collaboration and problem-solving ...... 66 Games as Deweyan worlds ...... 69 Play and Work...... 69 How do video games embody Deweyan principles of education? ...... 72 Summary ...... 88 The present study, its significance and research questions ...... 90 Chapter 3. Intervention design ...... 92 The design and development of the game...... 92 Essential principles of game design ...... 92 What is a game? ...... 92 Four constituent elements of games ...... 95 Developing Bond ...... 98 Design choices ...... 98 Technology ...... 108 Chapter 4. Method ...... 110 Research design ...... 110 Instruments and measures ...... 111 Procedure ...... 112 The setup and participants ...... 112 Student recruitment ...... 113 Assessments ...... 114 Intervention ...... 115 ix

General description ...... 115 Pilot session ...... 115 Sessions 2-4 ...... 117 Analysis...... 121 Chapter 5. Results ...... 125 Descriptive statistics ...... 125 RQ1: Did the game-based intervention improve students’ VS self-efficacy? ...... 129 RQ2: Did the game-based intervention improve students’ VS performance? ...... 131 RQ3: Did the game-based intervention improve students’ STEM performance? ...... 132 Chapter 6. Discussion ...... 134 Design Question: The principles of serious game design ...... 135 Research Question 1: Visuospatial self-efficacy ...... 138 Research Question 2: Visuospatial performance ...... 139 Research Question 3: STEM performance ...... 141 Limitations and future directions ...... 143 Bibliography ...... 146

List of Tables

Table 1. The 2x2 Framework of Visuospatial Ability...... 12 Table 2. The Differences and Similarities of Work and Play...... 71 Table 3. The Correspondence of Deweyan Principles of Education and Game World Design...... 74 Table 4. Game Design Decisions and the Underlying Theoretical Principles...... 98 Table 5. Challenges in Bond...... 101 Table 6. Game Challenges and the Problem-Solving Processes Involved...... 103 Table 7. Class Sections by Condition and Teacher...... 113 Table 8. Experiment Timeline...... 114 Table 9. Study Variables...... 121 Table 10. Sample Demographics...... 128 Table 11. Multiple Linear Regression Results: Descriptive Statistics...... 129 Table 12. Results of Multiple Regression Analysis (VSSE T2 as Dependent Variable) 130 Table 13. Results of Multiple Regression Analysis (VS performance T2 as Dependent Variable)...... 132 Table 14. Results of Multiple Regression Analysis (STEM grade (post) as Dependent Variable)...... 133

List of Figures

Figure 1. Classifications of Visuospatial Ability ...... 13 Figure 2. The Social Cognitive Model...... 28 Figure 3. Types of VR Equipment and Virtual Worlds ...... 36 Figure 4. Myron Krueger’s Videospace Project...... 40 Figure 5. AltspaceVR...... 41 Figure 6. Oregon Trail...... 42 Figure 7. Math Gran Prix...... 43 Figure 8. Sid Meier’s Pirates!...... 43 Figure 9. Civilization...... 44 Figure 10. SimCity...... 45 Figure 11. King Quest...... 46 Figure 12. Math Blaster...... 46 Figure 13. Some Weapon Categories in Dark Souls III...... 77 Figure 14. Minecraft...... 79 Figure 15. Monster Hunter: An Example of Acquired Resources...... 80 Figure 16. Dragon’s Dogma: Dark Arisen Quest Example...... 82 Figure 17. Dark Souls III: A Message from Another Player...... 84 Figure 18. Portal 2: An Example of a Puzzle...... 86 Figure 19. World of Warcraft Forum...... 87 Figure 20. Super Mario Odyssey Title Screen...... 96 Figure 21. Dark Souls III Title Screen...... 97 Figure 22. Code Pad (on the Left) and a Portal (on the Right)...... 105 Figure 23. Main Menu...... 106 Figure 24. Components of the Required Hardware for the Game...... 108 Figure 25. Classroom Setup for the Intervention...... 118 Figure 26. The Study Model...... 123 Figure 27. Participants' ethnicity...... 126 Figure 28. Participants' grade level...... 126 Figure 29. Participants' gender...... 127 Figure 30. Participants' videogame experience...... 127

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Chapter 1. Introduction

Imagine a situation in which you just moved to a new city and set out to explore your neighborhood in hopes of finding the closest grocery store nearby. You ask a neighbor for help and they give you a set of directions. What will be your next step?

You have a few options. First, you can memorize these directions, get in the car and follow the route as you recall it from memory. Some of us can do this with ease, although the task is directly proportional to the complexity of the route. Your second option is to enter the store address in a navigation app and follow the instructions of your device. This involves mapping the verbal information onto the real actions you need to perform as a driver (such as “in 500 feet, turn left”) or taking quick peeks at the map to visualize your relative position and progress en route. Some of us can follow verbal directions just fine; others will find themselves checking the map frequently to make sure they stay on the right track; a few people might have to rotate the map so that their direction on the map matches the direction the car is going.

Now, imagine that you are a first-year undergraduate student who decided to major in STEM (Science, Technology, Engineering and Math) and is taking introductory

STEM courses. Many of such courses require robust visualization skills to master conceptual information and to solve practical problems. You might complete them and continue onto the track of a STEM-related career. You might find yourself struggling and

1 switch to another STEM-related major; or your academic struggle might lead to dropping out of the field entirely.

What do these two imaginary situations have in common? Both of them involve the use of visuospatial (VS) thinking skills to some extent. These are the skills that we use to visualize, analyze and manipulate two-dimensional (2D) and three-dimensional

(3D) information and spatial relationships (Uttal & Cohen, 2012), often in the absence of clear visual clues or in the context of inferring 3D spatial information from 2D representations (such as assembling furniture based on a paper manual).

VS thinking is required for many tasks, from solving a Rubik’s cube to understanding molecule interactions and electric fields. For example, doctors extensively rely on their VS skills to analyze X-ray results; engineers - to set up equipment and read diagrams; chemists - to analyze molecular structures; geology and geography students - to study maps and phenomena such as landmass movement; architects - to analyze, design and draw physical spaces; artists and designers - to create art. Probably the most common use of VS thinking in everyday life involves navigation (especially areas unfamiliar to us) and following directions (e. g., when you ask a neighbor how to get to the closest grocery store). VS skills are the backbone of many everyday life and professional activities.

Just like some of us might struggle to get to a grocery store without aligning the map with the direction we are going, some students in the STEM field face obstacles when they are not prepared to deal with complex 3D and 2D information. An extensive body of research has shown a strong connection between VS skills and STEM

2 performance, retention rates, and the choice of STEM courses and career paths

(e.g., Cheng & Mix, 2014; Lubinski, 2010; Wai, Lubinski, & Benbow, 2009). The findings also show a consistent gender gap in VS skills (Halpern, Beninger, & Straight,

2011) which contributes to the gender gap found in STEM performance (Ganley,

Vasilyeva, & Dulaney, 2014). With the ongoing struggle to bring more females and other diverse populations in the STEM workforce (Committee on STEM Education of the

National Science & Technology Council, 2018), helping students improve their visuospatial skills has become a crucial task for educators. The good news is that VS skills are highly malleable and can be improved through practice (Terlecki, Newcombe,

& Little, 2008; Uttal et al., 2013). The bad news is that, although the importance of VS skills has been demonstrated by several decades of research, the incorporation of visuospatial training in the school curriculum is still lagging (National Research Council,

2006). This can partially be attributed to the complexity of visuospatial skills and their domain-specific manifestation (meaning, training materials ideally should be developed in discipline-specific contexts), and partially to the lack of comprehensive training materials for different levels of education.

The aim of this dissertation is to contribute both to the theoretical and practical pool of resources on visuospatial thinking. From a practical perspective, I propose a game-based intervention developed for middle-school students that can be implemented across various content areas. From a theoretical perspective, I propose the concept of visuospatial self-efficacy as the focal point of the intervention and argue that developing visuospatial self-efficacy might be an effective way to improve visuospatial thinking

3 skills. The intervention materials were created on the intersection of several theoretical frameworks, including the theory of self-efficacy, the educational philosophy of John

Dewey, research on visuospatial thinking, games and Virtual Reality in education, and the theory of game design.

The dissertation is organized as follows. Chapter 2 synthesizes relevant literature on visuospatial thinking skills, games and Virtual Reality in education, self-efficacy, visuospatial self-efficacy, pragmatism and the philosophy of John Dewey, and explores games as Deweyan worlds. Chapter 3 describes the design of the intervention materials.

Chapter 4 outlines the methodology of the study and the analysis. Chapter 5 presents the results of the data analyses, and Chapter 6 summarizes the main findings and provides their plausible explanations.

Chapter 2. Literature Review

Visuospatial skills

The Importance of visuospatial literacy in the 21st century

Visuospatial (VS) thinking has to do with the location and movement of objects and our own body, both in physical and mental space (Ontario Ministry of Education, 2014). It can be defined as a combination of three critical components (National Research Council,

2006):

• Concepts of space (ways to calculate distance, coordinate systems, understanding

the differences between 2D and 3D environments);

• Tools of representation (understanding relationships between views, laws of

projections, graphic design principles, and using a variety of modes and media

(including text, images, video, movements, gestures, etc.) to represent concepts of

space).

• Processes of reasoning (using concepts of space and tools of representation to

structure thinking about spatial problems).

The mastery of all three components is critical to success in many fields, including STEM subjects, physics, chemistry, geology, biology, medicine, computer science, arts, architecture and graphic design (Wai, Lubinski, & Benbow, 2009).

Surprisingly, it has not been reflected in the school curriculum. Despite robust evidence

5 pointing to the vital role of VS skills, VS instruction has not received much attention compared to other disciplines in North America (Ontario Ministry of Education, 2014).

Recently, there has been a call to incorporate spatial instruction at all levels of education to increase the spatial literacy of students (National Research Council, 2006; Ontario

Ministry of Education, 2014). To some extent, this trend can be attributed to the ongoing struggle of diversifying the STEM workforce and preparing more qualified specialists for the field (Committee on STEM Education of the National Science & Technology

Council, 2018). Another driving force beyond this movement is the spread of interactive technology and visually rich materials (from textbook graphs explaining molecular interaction to online charts demonstrating the coronavirus spread and the flattening curve), which necessitates a level of competence that allows people to make informed decisions by interpreting spatially presented information. As technology continues to change the workforce landscape by shifting the focus to more complex problems, VS reasoning is likely to keep making its way in the top skills required to engage in productive problem-solving at the workplace.

According to the National Research Council (2006), spatial literacy is a function of the following three factors:

• The habit of thinking spatially, i. e., knowing appropriate methods of spatial

thinking and in which contexts such methods should be applied;

• Having a toolkit developed to support spatial thinking; in other words, being

informed about how to practice spatial thinking. This includes knowing spatial

concepts and representations, spatial reasoning tools, and supporting technology

tools.

• Critical approach to spatial thinking: framing the spatial reasoning process within

the critical thinking approach (for example, critically evaluating spatial data,

checking its quality, and manipulating the data to engage in problem-solving).

Helping people develop VS skills, therefore, is a complex, interdisciplinary endeavor that should be informed by evidence from multiple domains of research. In the next several sections, I present the most relevant evidence that can support the development of a program aimed to improve VS thinking skills.

The definition and structure of VS skills

Before attempting to define VS skills, it is important to distinguish between the terms spatial ability and spatial skills. In this study, I use the term visuospatial ability to describe innate aptitude (following the ‘nature’ perspective) and visuospatial skills to refer to abilities acquired through training and experience (based on the ‘nurture’ perspective).

The complexity of VS (also termed spatial) thinking has caused a multitude of various approaches to its conceptualization, definition, and measurement. At a general level, VS thinking involves processing and manipulating visual and spatial information, such as object position, orientation, and arrangement (Hegarty, 2010; Hegarty & Waller,

2005; Höffler, 2010; Kell & Lubinski, 2013). More fine-grained definitions were

7 proposed based on psychometric studies, usually attempting to identify the factor structure of the construct. While researchers agree that VS skills are not a unitary construct, they disagree on their number and propose different naming conventions.

Lohman’s (1979) extensive analysis suggested a three-factor VS ability structure, including spatial relations (mostly based on mental rotation), spatial orientation

(visualizing an object from a different perspective), and visualization (visualizing complex object transformation, often without consideration of performance speed).

In one of the seminal meta-analyses on spatial ability, Linn and Petersen (1985) presented an overview of studies defining the structure of VS ability from the psychometric (based on measurement) and cognitive (based on cognitive processes underlying spatial problem-solving) perspectives. They concluded that spatial ability seems to be manifested in three salient factors.

The first factor is spatial perception, or how we perceive spatial relationships in different frames of reference. When we are walking, we make (often subconscious) judgments about the objects around us, including their size, position, and movement, and our own body in relation to the external world. As you are about to pass a group of people walking on the sidewalk, you make adjustments to your speed of movement and body orientation to make sure you do not bump into them. You raise your foot higher than usual to step over the curb or when using stairs. You change your direction of movement slightly to get around street signs and cars trying to exit parking lots. Without spatial perception, these seemingly simple and automatic actions would not be possible.

The second factor - and probably most studied in the literature - is mental rotation, or one’s ability to rotate a mental representation of an object in one’s head. In everyday life, mental rotation skills help us fit a long, heavy couch through a narrow doorway. When space is limited and the physical toll of trying out various orientations is substantial, visualizing what the couch looks like if we rotate it at a certain angle - the manifestation of the mental rotation skill - becomes an important problem-solving tool. In engineering, for example, students might need to imagine what an object would look like when rotated 90 degrees around an axis to draw or interpret a diagram displaying the object’s orientation.

The third widely reported factor of spatial ability is spatial visualization. While it overlaps with mental rotation and spatial perception in that it shares similar elements of spatial information processing, the focus is on the analytical nature of multi-step, complex spatial problem-solving. Glass blowing, for example, presents a complex spatial problem; it requires careful assessment of the current state of the glass material as well as the vision of a finished product to manipulate instruments in a way that leads to the desired outcome. In sports, volleyball players have to be cognizant of the space around them (the limits of the court, their position in relation to the limits); of the position and movement of other agents around them (is the hitter close to the net? If they are close to the net, where are they likely to direct the ball?); and of the position and movement of the ball (is adding a spin to the ball the right decision in the current situation? Does the current speed of the ball and direction of its trajectory justify one receiving technique over another?). Of course, players rarely have time to reflect on these questions and train 9 to respond to such situations almost automatically, but their reactions still rely on their spatial visualization skills. Linn and Petersen note, however, that this factor is hard to differentiate from fluid intelligence - the general skills of approaching problem-solving in creative and flexible ways.

Carroll (1993) in an extensive meta-analysis proposed yet another structure of VS ability comprising 5 major factors. Most of these factors have to do with the speed of spatial performance. They include:

• Visualization (manipulating visual patterns without consideration of the speed);

• Spatial relations (the speed of visual pattern manipulation);

• Closure speed (the speed of understanding an unfamiliar, obscure visual pattern);

• Flexibility of closure (the speed of identifying and understanding a familiar but

still obscure visual pattern);

• Perceptual speed (the speed of finding and comparing familiar, unobscured visual

patterns).

Höffler (2010) and Hegarty and Waller (2005) noted that the first two sub-factors have the most empirical support in the field. They defined the visualization sub-factor as understanding, encoding and manipulating objects’ position and orientation in one’s head regardless of the speed of these processes. Höffler distinguished between this sub-factor and that of spatial relations based on the difficulty of the mental transformations involved, whereby the spatial relations sub-factor is defined by simpler tasks and the speed of their completion. Hagerty and Waller defined the sub-factor of spatial relations 10 as understanding the arrangement of objects in a visual stimulus within one’s own frame of reference. These researchers also noted the discrepancies in the proposed factorial structure of the visuospatial thinking construct due to differences in measurement techniques.

Commenting on the inconsistencies of research findings from the past decades,

Uttal et al. (2013) proposed a new framework of categorizing VS abilities. Unlike previous classifications derived from psychometric studies, their approach draws on postulates of linguistics, cognition, and neuroscience. The framework is a 2x2 system classified along two dimensions: intrinsic vs. extrinsic information and static vs. dynamic tasks.

Intrinsic information is what we tend to use to describe any given object. If asked to describe a chair, you will likely note that it has four legs, a seat, and a back (parts) and that the back and the legs are perpendicular to the seat (relations between the parts).

When asked to point out where the chair is, you can say “it’s next to the desk”, thus using extrinsic information (spatial relations of objects). When objects in spatial tasks are not moving, such tasks are labeled as static. The tasks in which objects move or change their orientation (e.g., due to being rotated, cut or folded) are labeled as dynamic.

This classification is unique in terms of how it unifies a large body of developed assessments (see Table 1). In this framework, spatial tasks relevant to geoscience can be mapped onto the same structure as abstract mental rotation and water-level tasks despite the difference in their domain and scale.

Table 1. The 2x2 Framework of Visuospatial Ability.

Intrinsic Extrinsic

Intrinsic - Static Extrinsic - Static

Distinguish objects ignoring Perceive and understand the distracting information. relations of objects among each other or in relation to the Static environment.

E.g.: Embedded Figures tasks; E.g.: Water-Level tasks; Find a landmark on a map; Where is point A in relation to Identify the correct molecular shape; point B on the map?

Intrinsic - Dynamic Extrinsic-Dynamic

Mentally transforming objects, Visualizing the change in spatial including rotations, folding, cutting, relations among objects as a result perspective change and switching of movement or other from 2D to 3D and back. transformations

Dynamic E.g.: Mental Rotation Tasks; E.g.: Piaget’s Three Mountains Folding clothes to make them all fit in task; a small suitcase What sequence of events led to specific deformations in earth materials? How will you describe the path to a landmark if you move to the north and turn around?

Recently, Buckley, Seery and Canty (2018) proposed a heuristic framework of spatial ability based on the Cattell-Horn-Carroll (CHC) theory of human intelligence

(Figure 1). It includes a massive set of 25 factors divided into static and dynamic spatial factors and comprises factors related to speed, perception, memory, and other categories.

Figure 1. Classifications of Visuospatial Ability Overall, there is a general agreement that visuospatial thinking relies on a set of processes that, at the very least, include recognizing the visual stimulus, identifying visual patterns, encoding and holding mental representations of these patterns in one’s working memory, comparing patterns in terms of their position, rotation, shape, and composition, changing these characteristics of the patterns using mental transformations

13 and visualizing the result. To illustrate this, imagine that you need to assemble furniture

(a chair, for example) using a paper manual. You need to understand what parts of the chair are shown in diagrams; transform the 2D information in the manual into 3D information in your head and confirm your guess by comparing it to the real parts of the chair in front of you; hold that 3D mental representation of how the parts of chair fit with each other, are rotated and positioned relative to each other, your point of view and their origin; and fit them together using all of this information. In other words, you would engage in cognitive and motor processes that form spatial skills.

VS skills and academic outcomes in STEM

The positive link between VS skills and achievement in STEM disciplines has been documented extensively across various ages, both for adults (Kozhevnikov, Motes, &

Hegarty, 2007; Lubinski, 2010; Wai, Lubinski, & Benbow, 2009; Wei, Yuan, Chen, &

Zhou, 2012) and children and adolescents (Cheng & Mix, 2014; Gilligan, Hodgkiss,

Thomas, & Farran, 2018). Particularly:

• Better VS skills in preschool children predict better arithmetic skills in primary

school (Zhang, Koponen, Räsänen, Aunola, Lerkkanen, & Nurmi, 2014; Gilligan,

Hodgkiss, Thomas, & Farranm, 2018).

• Better VS skills in middle school predict better achievement in science classes

(Ganley & Vasilyeva, 2011).

• Adolescents with strong VS skills tend to choose to engage in STEM courses and

careers, while those with weaker VS skills prefer humanities and social sciences

(Shea, Lubinski, & Benbow, 2001).

• Some VS abilities have been linked to creative thinking in arts, humanities, and in

the STEM domain (Palmiero & Srinivasan, 2015).

• Students with weaker VS skills are less likely to choose a STEM major and, if

they do choose it, are more likely to have lower achievement and higher dropout

rates (Uttal & Cohen, 2012; Wai, Lubinski, & Benbow, 2009).

• Students with a high level of VS skills demonstrate more interest in STEM

subjects while those at a lower level tend to struggle even in entry-level STEM

courses and find class instruction less enjoyable (Lohman, 1988; Wai, Lubinski,

& Benbow, 2009).

The critical role of spatial skills in STEM was particularly highlighted by Wai,

Lubinski and Benbow’s (2009) findings showing that high-school students' spatial performance predicted their choice of STEM careers 11 years later - regardless of their verbal and math performance.

Why do VS skills have so much predictive power over STEM success? One immediately obvious reason is the highly spatial nature of many STEM disciplines.

Conceptual understanding of certain phenomena in physics (e.g., electric fields) and chemistry (e.g., the structure of molecules) requires visualizing three-dimensional (and often abstract) objects, their spatial relations and transformations. Some of the most famous scientific discoveries (such as the double-helix structure of DNA proposed by

Watson and Crick) hinged on visualizing complex objects and interactions. Apart from that, VS skills are essential to engage with many visual representations common in

STEM learning and work activities (including reading graphs, plots, diagrams, models and visualizations; Uttal, Miller, & Newcombe, 2013).

One of the critical problems related to the importance of VS skills in STEM domains lies in the unequal level of spatial expertise and performance between certain groups of individuals that translates into imbalanced representation in STEM.

Gender differences in VS ability

Gender differences in VS ability are among the largest and the most robust gender differences found in human cognition. Compared to small differences in verbal and mathematical performance (Lindberg, Hyde, Petersen, & Linn, 2010; Petersen, 2018), male advantage in VS skills, and especially mental rotation, is strong and persistent

(Halpern, Beninger, & Straight, 2011; Hyde, 2014; Kimura, 2000; Maccoby & Jacklin,

1974; Peters, Manning, & Reimers, 2007; Voyer, Voyer, & Bryden, 1995). Moreover, the gender gap in spatial skills translates into the gender gap in STEM by early adolescence

(Ganley, Vasilyeva, & Dulaney, 2014).

These findings naturally lead to the following question: What is at the root of these gender differences? Are there biological factors in place or can they be attributed to environmental and social influences? Three perspectives emerged to address this question

- the ‘nature’ perspective (attributing this gender gap to innate factors), the ‘nurture’

16 perspective (emphasizing the role of social and contextual factors) and the ‘nature- nurture’ perspective combining the two.

The ‘nature’ perspective on the gender gap in VS ability

Research has shown that sex differences in VS skills can be identified in infants as young as 3 months old. Quinn and Liben (2008) and Moore and Johnson (2008) demonstrated that male infants were able to differentiate between the mirror image of a familiar object

(which they perceived as novel stimulus and gazed at it longer) and the same object in a different orientation while female infants did not show any difference in gaze times. This lends at least partial support to the ‘nurture’ perspective of VS ability development

(although by no means indicates causal effects).

From this perspective, these differences emerged in the course of the natural evolutionary process (Geary, 2007; Gur & Gur, 2007; Haier, 2007). Certain cognitive functions and behaviors were favored in men in the evolutionary selection process but were not as relevant for women. Some examples include hunting (males) vs. gathering

(females) and higher intrasexual competition in males. In both cases males with better visuospatial skills had a reproductive advantage; better hunters could better provide for the family and thus were preferred by females, and in more current terms males are often more willing to trade off socialization goals (friends, family) to achieve high status in the society. Some research also points to the potential influence of hormones and using different brain hemispheres on the visuospatial ability (Halari et al., 2005; Rilea, Roskos-

Ewoldsen, & Boles, 2004).

The biological correlates, however, do not prove causation of the immutable nature of visuospatial skills, and the proposed biological explanations do not always stand up to scrutiny. Newcombe (2007) pinpointed the weaknesses of the theory of the evolutionary male advantage. For example, why was this metabolically low-cost trait equally not developed in both sexes when it could help them both better adapt to the environment? Why do we assume that the process of gathering did not require extensive use of VS skills while hunting did? What is the reproductive advantage of having varying levels of VS skills depending on one’s hormone levels (higher in women in the infertile part of the cycle and men with a lower testosterone level)? Moreover, research has shown that training in VS skills increases their level substantially for both men and women

(Terlecki, Newcombe, & Little, 2008; Wright, Thompson, Ganis, Kosslyn, & Newcombe,

2008). To sum up, whatever the nature of gender differences in VS abilities is, this perspective does not hold much value when it comes to education as it does not provide any tangible strategies of ameliorating the difference.

The ‘nurture’ and ‘nature-nurture’ perspective on the gender gap in VS ability

Another perspective put forward to explain the gender gap in spatial skills accounts for social and environmental factors.

Parents’ use of spatial language seems to be linked to children’s VS skills

(Polinsky, Perez, Grehl, & McCrink, 2017), and spatial play has been demonstrated to produce more spatial language in parent-child interactions (Ferrara, HirshPasek,

Newcombe, Golinkoff, & Lam, 2011). Parental use of spatial language might help

18 children to grow vocabulary and improve understanding of spatial concepts as well as provide exposure to various spatial problem-solving strategies.

Gender stereotypes can play a role in the gender gap as well. For example, when girls in middle school were made aware of the societal beliefs about male advantage in spatial thinking, they performed worse on mental rotation tasks (Neuburger, Ruthsatz,

Jansen, & Quaiser-Pohl, 2015). The influence of these beliefs may also be indirect. They may discourage girls from participating in spatial activities (which improve VS skills) and affect caregivers’ preference of activities they provide to children (i.e., give boys more opportunities to engage with spatial activities).

Extensive evidence points to the positive link between spatial experience and VS skills, particularly in the field of video games. With the emergence and growing popularity of video games, much research has been done to show the positive effect of playing video games on spatial skills (see Choi & Feng (2017) for a review). In particular, games can improve attentional visual field (Feng et al., 2007; Spence et al.,

2009), spatial selective attention (Spence et al., 2009), visuomotor coordination (Griffith,

Voloschin, Gibb, & Bailey, 1983), and visual memory recall (Ferguson, Cruz, & Rueda,

2007), and the improvements are long-lasting (Spence & Feng, 2010).

Lippa, Collaer, and Peters (2010) analyzed the data from over 90,000 women and

111,000 men from 53 nations and found that, surprisingly, the gender gap in spatial skills is most pronounced in countries with high gender equality and advanced economic development. They suggested that in more advanced and egalitarian economies women might be more exposed to scientific findings confirming the male advantage in spatial

19 performance, thus activating a stereotype threat response. A similar trend was observed with the female advantage in reading, and the opposite trend - or math. Halpern (2012) explains this by using the idea of “Matthew effect”: an initial advantage, however small, can accumulate to lead to future gaps. In other words, in egalitarian societies instruction can be aimed to improve spatial skills in both women and men but because both groups will grow at about the same rate, the gap will not be closed. This view represents the

‘nature-nurture’ approach to understanding the gender gap in VS ability. It acknowledges potential biological factors that may be at play but emphasizes the actionable steps we can take to minimize the social influences fueling the gender gap in VS skills.

Developing VS skills

Research suggests that VS skills can be developed through experience and training. Their malleability has been demonstrated by multiple interventions (Terlecki, Newcombe, &

Little, 2008; Uttal et al., 2013; Wright et al., 2008). Such interventions tend to produce large improvements, and some studies found evidence of long-term benefits of VS training and transfer effects (Feng et al., 2007; Pallrand & Seeber, 1984; Terlecki,

Newcombe, & Little, 2008). In the same vein, such training can improve STEM achievement, or example, in chemistry (Tuckey, Selvaratnam, & Bradley, 1991), calculus

(Ferrini-Mundy, 1987), physics (Pallrand & Seeber, 1984), engineering (Alias, Black, &

Gray, 2003) and geoscience (Piburn et al., 2005). Beyond the STEM field, VS thinking impacts other learning and cognitive processes, including memory (such as information

20 encoding and recall processes) and problem-solving (Golledge & Stimson, 1997; Kitchin

& Blades, 2002; Kulhavy & Stock, 1996; National Research Council, 2006; Verdi, 2002).

Spatial interventions can take on many forms and use different media. Uttal et al.

(2013) divided all spatial training studies into three categories:

1. Video game training: training was conducted through video games;

2. Course training: training was conducted in the form of a course;

3. Spatial task training: training focused on specific spatial tasks.

In practice, these types of spatial training can be represented by a large variety of tasks. For example, spatial task training can be done through repeated exposure to spatial tests (Alderton, 1989; Simmons, 1998), computer-based tasks (Turner, 1997; Yeazel,

1988), or with physical materials (Spencer, 2008; Sundberg, 1994). Course training might include courses related to VS thinking but not designed to teach this skill explicitly

(Pearson, 1991; Qiu, 2006) and those targeting the development of spatial skill as the main outcome (Lennon, 1996; Sorby, 2007). Finally, video game-based interventions usually rely on non-educational, often commercial games (many used Tetris, which is technically a non-educational game but is highly spatial in nature; Okagaki & Frensch,

1994; Sims & Mayer, 2002; Subrahmanyam & Greenfield, 1994; Terlecki et al. 2008).

As it appears from this extensive review, no studies used games developed specifically to improve spatial skills.

One of the problems in VS training lies in its complex nature and vast scope.

Spatial thinking is not a stand-alone subject area. It is a way of thinking permeating many other disciplines. On the one hand, VS skills rely on a set of general concepts that are

21 shared across domains - such as symmetry, reflection, orientation, and rotation. At the same time, it builds upon discipline-specific concepts (e. g., the definitions of small and large scale might be very different in geography and geometry).

We do not have enough conclusive evidence showing that spatial training in one domain (e.g., geography) transfers to better spatial performance in other domains (e.g., chemistry or physics; Khine, 2016), and in any given domain such training can take on very different forms. In an ideal world, each discipline would consistently integrate at least some elements of spatial training fostering both general and discipline-specific spatial skills. In reality, even though spatial thinking has received more attention in recent years, it is far from being integrated in the curriculum for all levels of education (National

Research Council, 2006). In part, it could be due to the complex nature of the phenomenon and the difficulty of integrating it across subjects; moreover, very few comprehensive VS skill development programs exist as of 2020.

One of the most well-known and systematic programs is the course developed by

Sheryl Sorby (Sorby & Baartmans, 2000), aimed at developing visuospatial thinking skills at the college level. It includes a textbook and a computer application to practice a variety of visuospatial skills, from visualizing solids and surfaces of revolution to mental rotations, symmetry, and cross-sections. Other methods reported in the literature are not very systematic and are usually not designed as a separate training course.

It is important to remember, however, that spatial skills build on both general and specific spatial concepts. Researchers and educators agree that incorporating context- specific spatial training (i.e., it would build on core concepts of each individual subject)

22 is an optimal way of improving VS skills (National Research Council, 2006) but creating discipline-specific training might be challenging due to regional and political differences

(at least in the USA owing to state-specific educational policies). However, it can be possible to create a program that could be used in any discipline with a minimal workload for teachers. Such a program will likely target the development of general spatial thinking and problem-solving skills.

At the same time, the current state of the literature on spatial skills calls for a critical reconsideration of the approaches and foundations of spatial training. As shown in this section, there is little consensus about the nature, structure, and even naming conventions of spatial skills. The mechanisms contributing to their development are also not well understood. Perhaps, instead of only targeting the improvements in spatial performance, we could leverage the concepts of learning psychology and motivation to increase students’ engagement in spatial tasks. The following sections explain this approach in more detail focusing specifically on the concept of self-efficacy.

Summary

Visuospatial (VS) skills involve processing and manipulating visual and spatial information, such as object position, orientation, and arrangement. VS skills are critical to

STEM success; they predict STEM achievement, the choice of STEM courses, majors and career paths, and retention. Research has consistently demonstrated the gender gap in

VS performance which also contributes to the gender gap found in STEM. It is possible

23 to ameliorate these differences through training, as the ‘nurture’ perspective on VS thinking suggests. However, VS instruction has been consistently neglected in the school curriculum, and few comprehensive VS training programs exist as of 2020. Moreover, researchers disagree on the definition, naming conventions, and number (ranging from 3 to 25) of spatial ability, and little is known about the mechanisms of VS performance improvement and transfer. Perhaps, instead of focusing on improving VS performance, we could shift to boosting students’ self-efficacy to engage with visuospatial tasks.

Self-efficacy

Self-efficacy as a theoretical construct

The theory of self-efficacy was developed by Albert Bandura; it directly links one’s engagement, learning and well-being to the type of experiences one is exposed to

(Bandura 1977; 1982; 1986; 1989). Self-efficacy is one’s belief that one can successfully complete a specific task.

According to the self-efficacy theory, when we engage in an activity and gain experience of succeeding at a specific task, through our appraisal of the result we become more confident in our capability to complete these types of tasks in the future. When confronting barriers, those who have previously succeeded will persist to achieve their goals. As self-efficacy increases, so does persistence in the face of difficulty. With more persistence, we are more likely to be successful at those tasks, and successful performance feeds into higher self-efficacy. This cycle repeats as we engage in the same or similar tasks over and over again, and can spiral up or down depending on how we interpret our performance. Our perceived self-efficacy depends on how we interpret four sources of information (Bandura, 1997).

Interpreting our physical and emotional response to the situation constitutes one of these sources (Bandura, 1997; Eccles, Midgley, & Adler, 1984; Seligman, 1990).

When a student feels anxious while trying to visualize a 3D object based on the given 2D views, they can interpret it as a sign of lack of competence, reinforcing their lack of self-

25 efficacy. It is also possible to interpret such anxiety in a positive light - as a normal part of growth through challenging problem-solving.

Verbal persuasion, or receiving positive feedback from others, can help the student realize that they have the capability to deal with this task (Bandura, 1997; Evans,

1989). Teachers’ praise or a friend’s reminder about past success can reinforce the students’ self-efficacy.

At the same time, the student can observe their friend solving the same problem and realize that, since someone with a similar competence level can solve it, they can solve it too. Bandura referred to this source of self-efficacy as vicarious experiences

(Bandura, 1997; Schunk, 1983).

The most powerful source of self-efficacy is interpreting one’s previous attainment - mastery experiences (Bandura, 1997). They have the most influence on one’s perception of self-efficacy because they provide the most immediate and direct evidence of one’s capability to achieve the desired result. The nature of such experiences determines their varying impact on one’s self-efficacy. If the student keeps struggling with similar tasks without having positive experiences of completing it, the chain of failures is likely to discourage future engagement with such activities. Alternatively, the student can break down the task in manageable steps, master each step, and by completing small sub-tasks gradually build their self-efficacy. Setting proximal goals

(that is, goals that are very achievable and rather hard to fail) is one of the most effective ways to develop self-efficacy.

The nature of experience, thus, determines one’s perceived level of self-efficacy.

But the experience is shaped by multiple factors. Bandura proposed the Social Cognitive model that explains the mutual influence of three sets of factors on each other: behavior, cognition, and environment (Figure 2). Their triadic reciprocal interaction determines one’s experience and the perception and interpretation of the experience. Through cognitive capacities (such as memory, planning, judgment and problem-solving) we can change our behavior and the environment. At the same time, our behavior has an impact on our cognitive processes and the environment, and the environment we are in may alter our thinking process and the way we act.

In the context of visuospatial thinking, behavior can be operationalized as engagement/disengagement with VS tasks and subsequent performance; cognition as the problem-solving strategies; and the environment as the context in which VS tasks are presented, how they are structured and framed. For example, if a student is presented with a challenging VS task without proper support in the form of breaking the task down into smaller manageable steps (environmental factor), and the problem-solving strategies the student is using do not match the task (cognitive factor), they might choose to disengage with the task and receive a low grade (behavioral factor). The combination and mutual interaction of these factors then would likely lead to a lower level of self-efficacy for this type of VS tasks.

Figure 2. The Social Cognitive Model.

Such situations are problematic because self-efficacy is one of the strongest predictors of the core actions leading to productive learning - starting a task, persisting on the task in the face of adversity, completing the task and engaging in similar types of tasks in the future (Bandura, 1986). If failing at completing the VS tasks becomes a pattern for the student, they might start putting off doing homework involving related tasks, giving up easily whenever difficulties come up or drop out of the course entirely.

Self-efficacy is also a context-specific construct, meaning high self-efficacy in math or physics, for example, might not necessarily translate into visuospatial self-efficacy

(VSSE). With many STEM courses relying on the high level of VS skills, VSSE may be one of the factors that make a difference between dropping out and continuing one’s

STEM academic career.

Visuospatial self-efficacy

Visuospatial self-efficacy is an understudied construct in the educational literature. The scarce evidence currently available from the research on the concept only shows that visuospatial self-efficacy has a significant correlation with performance on spatial ability tests (Towle et al., 2005).

Based on Bandura’s definition of self-efficacy, I define VSSE as one’s perception that they can complete a task involving the use of visuospatial thinking skills. A lot of studies measured other types of self-efficacy, including academic self-efficacy (Falco &

Summers, 2019; MacPhee, Farro, & Canetto, 2013), math self-efficacy (Campbell &

Hackett, 1986; Pajares & Graham, 1999; Pajares & Miller, 1994), and science self- efficacy (Britner & Pajares, 2001; Britner & Pajares, 2006), but almost no studies focused on the role of visuospatial self-efficacy. This might be partly due to the lack of an instrument to measure it, although there have been a couple of attempts to develop one.

Safadel, White and Ghasemi (2018) created what they called the Spatial Ability

Self-Efficacy Scale (SASES) based on a mental rotation test by Vandenberg & Kuse

(1978). They asked the participants to rate their confidence level in solving a very specific VS task from the mental rotation test on the scale from 0 (Not confident) to 10

(Completely confident). They reported a high level of Cronbach's alpha and a moderate positive correlation of the VS self-efficacy scores with the scores on another test measuring participants’ spatial ability. They did not perform Exploratory and

Confirmatory Factor Analysis to analyze the underlying constructs measured by the scale.

Paunonen and Hong (2010) approached measuring VS self-efficacy in a slightly different way. They measured the spatial ability of participants by using the Spatial subscale of the Multidimensional Aptitude Battery (Jackson, 1984) consisting of 50 mental rotation problems. To measure spatial self-efficacy, the respondents were made aware that they were working with the spatial ability test, shown 5 examples of the problems comparable to those on the test, and asked to estimate how they think they would score if they were to take this test with a large number of similar problems. They averaged the reported score by dividing it by the number of items on that hypothetical test.

West, Welch and Knabb (2002) used a Spatial Self-Efficacy Questionnaire

(SSEQ) to measure participants’ self-efficacy in remembering object locations, routes in buildings and city spaces, and spatial arrangements of various spaces. The scale consists of 8 subscales that fall into a single factor, with each subscale consisting of 5 questions.

Kinsey, Towle, O'Brien and Bauer (2008) used a web-based questionnaire comprising 20 questions that were modeled after the Purdue Spatial Visualization Test:

Rotations (PSVT:R; Guay, 1977). First, participants were introduced to 3 sample questions that explained the instructions for taking the questionnaire. Then, they answered 20 questions. In each question, participants were presented with 2 images of an object. In the first image, the object was in the initial orientation. In the second, the same object was rotated. Both images were presented for 3 seconds and then disappeared, allowing participants to see the relative position and orientation of the objects without

30 being able to discern the performed mental rotation. Then, a second, new object was shown in a different initial orientation. Only one image with the initial orientation was presented without any time restrictions. Participants then rated their confidence on the scale from 1 (not at all confident) to 7 (extremely confident) in rotating the second object in the same manner as the first object was rotated to achieve the result in the second image. The authors mentioned that the questionnaire was being validated at the time of writing. No validation results are available as of now.

These scales heavily rely on very specific spatial ability tests; their factor structure remains unexplored or unreported; most of them are dominated by the VS task of mental rotation; and surprisingly none of the scales are available for use. For the purposes of this study, my research team and I developed a VSSE scale to address these limitations. The scale will be described in more detail in Chapter 4.

VSSE as a foundation for spatial interventions

Despite decades of research, visuospatial skills are still a rather ambiguous concept with little consensus on their structure and definition. Spatial training can yield substantial improvements in spatial skills and some studies show that the effects can transfer at least to some types of new tasks, although the extent of the transfer requires more systematic analysis (Uttal et al., 2013).

One of the goals of this dissertation is to propose a new way of approaching VS intervention development. While performance is an important metric of progress, it is 31 dependent in active engagement and persistence. Self-efficacy (and, as I propose, VSSE by extension) is a potent predictor of both (Zimmerman, 2000). Developing students’

VSSE in general VS problem-solving might be an effective way to improve their performance by increasing the likelihood of active engagement with VS tasks.

First, it might open up a wider range of spatial activities that students would be willing to engage in, and, provided they perceive their experience as successful, create a positive feedback loop in which their success feeds into higher self-efficacy, which creates more interest and engagement in spatial tasks. Due to the lack of research on

VSSE, we do not have much evidence about the extent to which it could transfer between tasks, but there is plenty of evidence showing the transfer of self-efficacy between similar tasks (Zimmerman, 2000) and limited evidence of the self-efficacy transfer from one domain to others (Massar & Malmberg, 2017).

Second, focusing on more generic VS problem-solving can help eliminate the barrier of domain specificity that requires educators to develop different materials for each educational context. An intervention that integrates various types of tasks and emphasizes the problem-solving process as opposed to domain-specific algorithms can be integrated across various educational and social contexts without adding extra work for teachers.

The current dissertation research, to my knowledge, represents the first attempt to approach visuospatial training from the self-efficacy perspective in a practitioner-oriented way. The main challenge within this approach is both theoretical and practical in nature.

Namely, there are no available intervention or curricular activities built on such

32 principles. This calls for the development of theoretically and practically sound VS intervention materials, which poses yet another problem: What should such intervention/curricular materials look like and how do we create them?

The proposed solution builds upon recent technological advances as well as philosophical principles of education. The following sections outline each of these components in more detail.

Summary

Self-efficacy is one’s belief that one can successfully complete a specific task. The appraisal of personal performance can change one’s level of self-efficacy in a given domain. Self-efficacy is a powerful predictor of task choice, engagement, persistence, and task completion. Perceived self-efficacy draws on the interpretation of four sources of information (from lower to higher impact): (1) physical and emotional response; (2) verbal persuasion (e. g., praise); (3) vicarious experiences (observing the performance of role models); and (4) mastery experiences (directly engaging with a task). Setting proximal goals (that is, goals that are small and easily achievable) to create positive mastery experiences is one of the most effective ways to increase self-efficacy.

Visuospatial self-efficacy (VSSE) is the type of self-efficacy specific to the domain of

VS tasks. Very few studies investigated this concept but there is evidence showing a strong correlation of VSSE with VS performance. I argue that VSSE can serve as a foundation of VS interventions. Instead of focusing on improving VS performance, we

33 could aim to develop VSSE to increase overall engagement and persistence in VS tasks.

Addressing the general VSSE as opposed to context-specific VSSE might be an effective way to increase students’ engagement with VS tasks in general and remove the barrier educators face due to lack of context-specific spatial training materials.

Games and Virtual Reality as a medium for VS training

The history of games and Virtual Reality in education

The past several decades have been marked by rapid advances in technology. The world witnessed tremendous growth in computational power accompanied by the shrinking size of devices, decrease in technology cost and increase in its accessibility. Education has been lagging behind the technology development curve in adopting innovations in the classroom, and is only starting to catch up and develop a deep understanding of productive use of technology for teaching and learning (Glassman, 2016).

Among the most promising recent technologies are video games and Virtual

Reality (VR). Video games have been part of our society for at least several decades now while Virtual Reality became relatively accessible for the general population only a few years ago. More and more researchers and educators started to investigate the possibilities of these virtual worlds. In this section, I review the history of these two technologies and show how they can be used as a medium for the proposed VSSE intervention.

Defining Virtual Reality and serious games

Virtual Reality (VR) might be one of the vaguest terms used in educational literature. It can refer to anything from desktop-based online virtual worlds, known as Multi-User

Virtual Environments (MUVEs), to massive multiplayer online role-playing games

(MMORPGs), to CAVEs (cave automatic virtual environments in which users are

35 looking at the displays placed on the walls around them) and various types of Head-

Mounted Display (HMD) VR (Jensen & Konradsen, 2017; Figure 3). It can be presented on phones, desktop computers, or using special equipment (in the case of CAVEs and

HMDs). It can be online and multi-user or offline and single-user, as well as immersive and non-immersive. With all of these factors combined, it becomes especially important to specify what type of VR is being discussed and why/how it is different from other VR technologies.

Figure 3. Types of VR Equipment and Virtual Worlds

In the current manuscript, whenever I use the term VR, I will focus specifically on immersive, stereoscopic Virtual Reality, which I define as a computer-generated virtual space that provides the user with sensory feedback (the environment changes based on the user’s sensory data input, such as movements) and allows the user to interact with the environment (Sherman & Craig, 2003). HTC Vive, Oculus Rift and Google Cardboard are just some examples of stereoscopic devices that can be used to create immersive VR experiences. Non-immersive VR, on the other hand, is usually presented on a computer screen, and users perceive both the real and the virtual world together at the same time.

Second Life (SL) is one of the most popular examples. The term “immersive” is less than ideal for differentiating different types of VR because, although it might be relatively accurate in terms of physical immersion, the lack of psychological immersion in “non- immersive” VR can be contested. Whenever there is a need to refer to non-immersive virtual worlds, I will use the term Virtual Environments (VE). When such environments are online and multi-user, I will refer to them as Multi-User Virtual Environments

(MUVEs). Finally, I will use the term virtual worlds as an umbrella term for all types of

VR and MUVEs technologies discussed above. Thus, Second Life is a MUVE, Google

Cardboard is mobile-based VR, and both can be referred to as virtual worlds.

To narrow down the scope of the review in the current manuscript, I will focus on a special type of games known as serious games. The terminology surrounding this concept is also rather ambiguous. Several overlapping terms have been circulating in the literature on games in education (Susi, Johannesson, & Backlund, 2007).

Serious games can be defined as (usually digital) games that have a purpose broader than or different from simple entertainment. Such games are not always created for educational settings. For example, PeaceMaker was developed by a small team of students at Carnegie Mellon University in 2007 and intended for a general audience to understand the Israeli–Palestinian conflict. Some commercial games can also be used as serious games (e.g., Civilization by Sid Meier).

Game-based learning can be considered a branch of serious games with a specific focus on educational game applications; on the other hand, as an educational approach, it is broader than serious games as it might include the latter as one of its elements.

Digital game-based learning is game-based learning implemented specifically on digital platforms (e.g., computer-based games as opposed to board games).

Edutainment refers to educational methods that “dress up” regular classroom or learning activities to make it look like entertainment. An example of this is a math video game called Prodigy. While the game looks like a regular game on the surface, students who play it essentially do the same things they would typically do in the classroom - i. e., solve math problems. Such games tend to be “chocolate-covered broccoli” (a term proposed by Brenda Laurel in her 2001 book Utopian Entrepreneur) in that they sugarcoat drill-based activities but the essence of the activities does not change.

E-learning happens in computer-mediated environments, especially through the

Internet. Some other relatively equivalent terms include computer-based learning, computer-enhanced learning, and distance learning.

For this literature review, I will focus on serious games and digital game-based learning in a broad sense - including digital games developed specifically for educational or non-entertainment purposes and commercial/entertainment games used for educational purposes.

The history of Virtual Reality and games

Virtual Reality: the path to accessibility

Although Virtual Reality became relatively affordable and available to the public only in the 2010s, the actual history of VR development dates back to the 1960s. Many events, discoveries, and technology breakthroughs guided VR development over the past sixty years (Sherman & Craig, 2003).

In 1960, Morton Heilig patented a stereoscopic television apparatus which was similar to the concept of Head-Mounted Displays (HDMs) - the key element of any VR setup. This was followed by a development in computer graphics software with Ivan

Sutherland creating Sketchpad, the first interactive computer graphics application, in

1963. Just a year later, Sutherland proposed a concept of a display which can serve to interact with non-physical, virtual objects - a core element of VR experience. 1976 saw

Myron Krueger’s artificial reality project called “Videospace”

(https://youtu.be/dmmxVA5xhuo; Figure 4) that was much closer to the way we experience VR nowadays in terms of its interactivity and immersion - even though the experience was not immersive in the traditional sense (users were not wearing HMDs).

Users were able to interact with different elements of the environment (for example, type by pressing letters, draw and resize shapes, interact with a critter and other users) and were represented as silhouettes on the screen. Technological advances kept paving the way for VR in the mid-70s and mid-80s but the term Virtual Reality was proposed only in 1984 by Jaron Lanier - the founder of VPL Research, Inc, an organization that later announced RB-2 - the first complete VR system.

Figure 4. Myron Krueger’s Videospace Project. From the 90s and up until now, VR developments were mostly focused on improving its performance capabilities and financial accessibility. The first time an HDM was offered for less than $10,000 was in 1991; in just four years, the price dropped to a thousand dollars, and in 2012 it was possible to buy an HMD for about $300 - and the price plummeted to 5-20 dollars with the introduction of Google Cardboard. In the meantime, new types of VR were emerging. CAVE (Cave Automatic Virtual

Environment) VR was introduced in 1992, and mobile VR became more and more widespread with the emergence of smartphones and their progressing technical specifications. Currently, Virtual Reality seems to be developing along the lines of multi- user interactions (e. g., Sansar by Linden Lab and AltspaceVR (Figure 5)), utilizing mobile platforms, and advancing motion tracking techniques to provide a comfortable experience without typical side effects (such as cybersickness).

Figure 5. AltspaceVR.

Serious games: from Oregon Trail to multiplayer online games

According to the comprehensive account presented in Tobias and Fletcher (2011), the roots of the first educational digital games can be traced to the 1970s when students at

University computer systems started creating games for the PLATO computer-based instruction system (such as Airfight - a gamified 3D flight simulator). In 1972, students at

Carleton College in Minnesota developed Oregon Trail (Figure 6), a text-based game that 41 focused on exploring the American migration processes in the 19th century through typing commands and managing resources. It was one of the first digital serious games to be used in the school context, and the series is still widely popular nowadays.

Figure 6. Oregon Trail. In 1967, Daniel Bobrow, Wally Feurzeig, and Seymour Papert (MIT) developed

Logo, a programming language designed to introduce children to concepts of coding and math. Later, they proposed the idea of microworlds - “self-contained computer-generated interactive worlds designed to model complex systems” (Tobias and Fletcher, 2011, p.

20) which could be used to help players explore physically or otherwise unavailable environments in a safe and controlled manner. This idea was foundational to the field of educational game design.

From the 70s to the 80s, with the development of early game consoles, more educational games started to emerge, including Math-A-Magic, Math Gran Prix (Figure

7), and World Zapper. Most of them followed a drill-based approach; players were

42 supposed to perform certain operations; correct solutions were rewarded, incorrect were punished. In the 1980s, game consoles and personal computers started to become a worldwide phenomenon, and the rise of game design software propelled the growth of the amateur game design development movement. Some representative games of that time include Sid Meier’s Pirates! (Figure 8) and Lunar Leeper which successfully blended learning with entertainment and became a generational phenomenon.

Figure 7. Math Gran Prix.

Figure 8. Sid Meier’s Pirates!.

The 80s saw a rise of simulations; their more open-ended and less gamified nature made it easier to introduce such games to formal schooling environments. One of the most well-known simulations of the time was Civilization (Figure 9), a turn-based game in which players needed to make decisions about politics, agriculture, science, and culture to develop a long-lasting civilization. The game adapted to players' decisions, allowing them to explore multiple potential scenarios and outcomes. In 1989, Sim City

(Figure 10) was released, a game that centered around constructing city maps. While not entirely realistic, it proved to be a useful tool to learn about city infrastructure and development. SimCity and Civilization paved the way to other simulations, including

Zoo Tycoon and Sim Earth.

Figure 9. Civilization.

Figure 10. SimCity. The 1990s saw the development of edutainment. It was popularized by interactive adventure games such as Space Quest, King Quest (Figure 11), Police Quest, and Maniac

Mansion. These games popularized interactive storytelling in which players could experience the game story through interaction with the game elements. Edutainment viewed schools as a lucrative business venue. Math blaster (Figure 12) and Jumpstart!

Adventures were prominent edutainment games at the time. Similar to the earlier edutainment examples, such games usually followed a drill-based model and were not as fleshed out as commercial games. Ultimately edutainment companies’ profit did not reach predicted levels, partially due to strong competition of entertainment games with ground-breaking technologies and graphics, as well as due to their lack of consideration of the role of the teacher in edutainment game use.

Figure 11. King Quest.

Figure 12. Math Blaster. The past two decades were characterized by the focus on multiplayer online games and virtual worlds, mobile gaming, Virtual and Augmented Reality games, and serious games. The serious games movement started to take shape with the introduction of Virtual-U and MIT’s Games-to-teach project, as well as the first Serious Games 46 summit in 2003 whose participants were interested in using games for social good as opposed to mere entertainment.

Due to the novelty of VR technology and the relatively recent establishment of the serious games field, there is not much information about their effective design and integration in education. The next sections present available evidence form limited research on the effectiveness of these technologies.

VR and games in education

Merchant, Goetz, Cifuentes, Keeney-Kennicutt, and Davis (2014) presented one of the most extensive reviews of the effects of games and virtual worlds on teaching and learning. Their review focused on three broad categories - games, simulations and virtual worlds. They found that game-based learning was more effective than learning through simulation and virtual worlds but concluded that all three types of media are suitable for acquiring knowledge, abilities and procedural skills. Games were shown to produce long- lasting learning benefits although the question of transferability of the effects remained unanswered; the authors also noted that when students played games over a more extended period of time, their learning gains started to decline, suggesting the novelty effect.

Connolly, Boyle, MacArthur, Hainey and Boyle (2012) reviewed 129 studies on games in education and analyzed their distribution based on the domain, measured outcomes, and effectiveness. They found that games have been used across multiple

47 domains; the health domain was most active in game implementation (probably due to a long history of using simulations to improve specific procedural skills, for example, in surgeons), followed by social sciences, STEM, language and history domains. Most serious games in these studies sought to improve knowledge acquisition and content understanding, followed by perceptual and cognitive skills, while entertainment games were most often used to boost motivation and affective outcomes.

Games have been shown to improve a variety of outcomes, including:

• Academic performance and motivation (Di Serio, Ibáñez, & Kloos, 2013; Holley,

Hobbs, & Menown, 2016; Martín-Gutiérrez & Meneses, 2014; Sotiriou &

Bogner, 2008; Ventura, Shute, & Kim, 2012);

• Spatial skills (Green & Bavelier, 2007; Spence & Feng, 2010);

• Analytical, strategic, problem-solving and decision-making skills (Curtis &

Lawson, 2002; Sánchez & Olivares, 2011);

• Learning and recollection capabilities, short-term and long-term memory (Blacker

& Curby, 2013; Mitchell & Savill-Smith, 2005);

• Psychomotor skills (Borecki, Tolstych, & Pokorski, 2013; Kennedy, Boyle,

Traynor, Walsh, & Hill, 2011);

• Visual selective attention (Boot, Kramer, Simons, Fabiani, & Gratton, 2008;

Green & Bavelier, 2003);

• Social skills such as collaboration and shared decision-making (Kaufmann,

Steinbugl, Dünser, & Gluck, 2005; Martin-Gutiérrez, Saorín, Contero, Alcaniz,

Perez-Lopez, & Ortega, 2010; Sánchez, Mendoza, Salinas, 2009).

Overall, research has shown that games and VR can improve a multitude of academic, motivational, and cognitive outcomes. However, such studies are skewed towards certain domains (such as media arts, health, and environment), often conducted in university settings, and are descriptive rather than experimental (Hew & Cheung,

2010). There is a need to investigate and explain how games can be effectively incorporated in teaching and learning (Susi et al., 2007).

Games and VR in visuospatial interventions

With the increasing availability of advanced technology, virtual worlds and games became a fruitful ground for multiple VS interventions (e. g., Dünser, Steinbügl,

Kaufmann, & Glück, 2006; Kaufmann, Steinbügl, Dünser, & Glück, 2005; Lee, Chen, &

Chang, 2016; Martin-Dorta, Saorin, & Contero, 2011; Passig & Eden, 2001; Rafi &

Samsudin, 2009; Samsudin, Rafi, & Hanif, 2011). VR has gained particular prominence due to its three-dimensional nature and interactive capabilities. Some have claimed VR has the potential to revolutionize education (Gadelha, 2018; Jensen & Konradsen, 2017) due to its inherent flexibility, including interactivity and open-ended, sandbox type of visually rich environment that can be used to design and perform practically any type of activity - especially dangerous, expensive or impossible to implement in the real world

(Johnston, Olivas, Steele, Smith, & Bailey, 2018). In the context of VS interventions,

VR's potential for learning is often viewed in light of its 3D nature of visualization and the opportunities to change the angle of one’s view and to manipulate objects’ rotation and position - essential skills in VS thinking. 49

The practical results of VR implementation in this capacity, however, are mixed.

Some studies (for example, Carbonell-Carrera & Saorin, 2017; Molina-Carmona,

Pertegal-Felices, Jimeno-Morenilla, & Mora-Mora, 2018) found positive results of using

VR as an instructional tool in improving students' VS ability, while others (e. g., Dünser,

Steinbügl, Kaufmann, & Glück, 2006) concluded that there is no compelling evidence supporting the effectiveness of VR. These results mirror those obtained from other STEM domains and in relation to other types of technology. The studies that explore VR influence on learning outcomes (including cognitive, affective and psychomotor skills) often demonstrate mixed results (effectiveness varies from task to task) or no significant differences in comparison with other types of instructional media (Jensen & Konradsen,

2017). Moreover, there is a lack of understanding of how to use VR from a pedagogical perspective. For example, Mikropoulos and Natsis (2011) analyzed 53 empirical studies from 1999 to 2009 (most of them focused on non-immersive virtual environments) and found that most research centered around students’ affective responses to the use of technology. Kavanagh, Luxton-Reilly, Wuensche and Plimmer (2017) added that many educators use technology guided by their beliefs about its potential benefits but without any clear pedagogical guidelines or principles of its curricular integration.

The research on games and spatial cognition, on the other hand, shows more consistent results. Video games have been shown to improve attentional visual field and performance on basic and complex spatial tasks with a long-term effect (Spence & Feng,

2010); mental rotation skills (Cherney, 2008); navigation skills (Shute, Ventura, & Ke,

2015); eliminate the gender gap in spatial attention and decrease the gender gap in mental 50 rotation ability (Cherney, Bersted, & Smetter, 2014; Feng, Spence, & Pratt, 2007). Most studies used commercial or non-educational games for spatial interventions (Uttal et al.,

2013).

There are several potential reasons for the mixed results of the use of VR for spatial training but not in games. First, a more passive and less interactive approach to engagement with VR technology (due to more hardware and software constraints).

Games can be played using a standard computer setup (laptop or PC, keyboard and mouse, or controller), and with the growing computer power, they tend to feature more and more complex worlds and interactions. Accessible mobile VR might not have any control input (i.e., users are limited only to looking around) or rely on (mostly) Bluetooth controllers which have a more limited range of options compared to standard console controllers. Second, both students and teachers have less familiarity with the VR technology due to its relative novelty (games have already become a normal part of everyday life). The devices required to operate VR are often non-intuitive, and setting them up and troubleshooting problems require a high level of technology skills. Third, cybersickness (feeling dizzy and nauseated in virtual environments) is a common side effect of using VR (Davis, Nesbitt, & Nalivaiko, 2014), which makes it harder to productively engage in the VR-mediated learning process. Finally, the lack of understanding of educational design principles to inform the development of effective

VR integration is still one of the biggest obstacles to effective VR integration (Kavanagh et al., 2017).

Based on past findings, in the current study, Virtual Reality was integrated as part of the game developed for the intervention as opposed to a stand-alone medium. In the next section, I explain the theoretical principles that became the foundation of the intervention game design.

Summary

I define Virtual Reality (VR) as a computer-generated virtual space that provides users with sensory feedback and allows them to interact with the environment - what is typically known as immersive VR. Serious games are (usually) digital games that have a purpose broader than or different from simple entertainment. Research has shown that both games and VR can lead to improvements in learning outcomes, cognition, motivation, and communication, including academic performance, problem-solving, short-term and long-term memory, psychomotor skills, visual selective attention, collaboration skills, and more. They can also significantly improve VS skills, although games have shown more consistency in producing tangible improvements in this domain.

The theoretical principles of designing a game-based VS intervention

According to the self-efficacy theory, one of the main sources of our appraisal of self- efficacy lies in mastery experiences. Within the scope of this dissertation, it means that learning experiences should be shaped in a way conducive to the development of VSSE.

This begs the question: how do we create such experiences?

John Dewey, an American philosopher of the 20th century, dedicated his life to answering this question. In his works, he tackled the nature and criteria of true educational experience and its relation to the larger social context of everyday life and democracy. He was also an early founder of pragmatism, a prominent philosophical movement in the US in the 20th century. In the following sections, I provide a brief overview of the main principles of pragmatism to lay a foundation for understanding the context of Dewey’s views and explain Dewey’s take on the nature of educative experience.

The philosophy of pragmatism

Essential principles

Pragmatism as a term was introduced by an American philosopher William James in his lectures in 1898 - the idea of which he credited to Charles Pierce. Although some of its precursory ideas could be found in the philosophy of the past centuries, it was formed as a movement with a defined set of principles around the early 1900s. Three main philosophy figures defined the development of this movement: Charles Peirce, William

James, and John Dewey (Bacon, 2012). 53

The ideas of pragmatism were initially developed through discussions of the

Metaphysical Club - an informal, short-lived conversation group formed by Pierce,

James, and Oliver Holmes (who would go on to be an Associate Justice of the Supreme

Court) in the early 1870s. Recent political and social events had a great influence on their ideas, including widening the franchise, professionalization of philosophy in the US, and the American Civil War (1861 to 1865). The latter, in particular, left a scar on American history and the minds of its thinkers. James and Pierce saw the war as a result of a rigid mindset focused on certainties, which led to their distrust of the absolute truths. They recognized the destructive power of ideas that had turned into absolute ideologies

(Bacon, 2012).

As a result, pragmatism in a broad sense does not seek to establish a rigid inquiry framework; it aims to provide tools to cope with the world. One of its main premises rests on the idea that humans are not only natural but also social creatures, and that our practices are sufficient to address tasks in our life, although we should account for context-specific evidence to revise these practices as we go.

Pragmatists rejected the notion that universal objective truth that can be somehow

‘revealed’ to us. Pragmatism aimed to approach problem-solving without involving any metaphysical questions. The human mind does not reflect the truth; it is not a mirror but an active actor and the co-creator of truth (Menand, 2002).

In everyday life, we constantly have to make choices. What is right to do? What is true and what is false? For example, should you stop your friend from doing something

54 that you know will have bad consequences? There are no predetermined notions of truth to answer this question. We do not know these notions before we go through a thinking process taking into account multiple contextual factors. How close are we with this friend? How serious are the potential consequences? Will the friend listen to us? Have they made a lot of bad choices in the past? Can this experience help them learn from mistakes? Depending on the answers, we can choose to stop the friend or not. Truth becomes contextual, not absolute.

While some of the principles of pragmatism are shared between pragmatists, their individual stances vary quite a lot. In the current manuscript, I will focus on the legacy of three classical pragmatists: Pierce, James, and Dewey.

The pragmatism of Charles Pierce

Charles Pierce had a background in chemistry and scientific line of work. He approached philosophy from the perspective of the scientific method. Pierce’s theory of pragmatism can be considered narrow as he believed it should be used to fix conceptual meanings through the scientific method.

In Some consequences of four incapacities (1868), Pierce criticized the postulates of Descartes’ philosophy. According to Descartes, we should doubt all of our ideas and beliefs. To reject them, all you need is to find some reason for doubt (Descartes, 1996, p.

12):

Reason now leads me to think that I should hold back my assent from opinions

which are not completely certain and indubitable just as carefully as I do from

those which are patently false. So, for the purpose of rejecting all my opinions, it

will be enough if I find in each of them at least some reason for doubt.

Pierce claimed that we never start our inquiry as a blank slate; we hold certain prejudices and biases which often go unnoticed and unquestioned. Achieving certainty through individual consciousness, thus, becomes impossible, leading Pierce to the conclusion that philosophy should follow the example set by science and employ scientific methods.

He further clarifies the necessity of a scientific method as a foundation of philosophy in The fixation of belief (1877). For Pierce, inquiry stems not from our desire to reach the truth but to avoid doubt - an unpleasant feeling of dissatisfaction we feel when acting on our beliefs does not bring an anticipated result. Striving to end doubt, we resort to inquiry to arrive at belief. Pierce described four methods of establishing beliefs.

The first is the method of tenacity, which is simply sticking to your existing beliefs rejecting all evidence that challenges it and avoiding situations that subject your belief to scrutiny. For example, a person that believes that the Earth is flat might dismiss the images of our planet from space which demonstrate the false nature of their beliefs as a government conspiracy plot and gravitate towards groups of people sharing the same belief.

The second method is similar except it is used by authorities on a larger scale

(countries, nations) - thus termed the method of authority. George Orwell provided an

56 excellent example of it in a world-famous dystopian novel 1984, in which the government proclaims the following dogmas:

war is peace

freedom is slavery

ignorance is strength

Any challengers of these dogmas were to be prosecuted and “corrected”.

Eventually, one of the characters remarks that the government might as well proclaim that 2 by 2 equals 5, and with enough propaganda and enforcement people will hold it as a true belief. Even though in the book the fate of the main dissident character is quite tragic, it highlights one of the weaknesses of this method of establishing beliefs proposed by Pierce: there will always be individuals who reject enforced beliefs, and such beliefs tend to be unsuccessful in the long term, challenged by new generations and changes in authority structures and power. Moreover, it is not possible for an institution, no matter how large and influential, to fix all possible beliefs simply due to their sheer number and variety.

The apriori method of fixing beliefs relies on choosing beliefs that are agreeable to reason. We can use pure reason to arrive at conclusions ignoring or dismissing empirical evidence. Such beliefs, however, are largely culture-specific and dependent on the current fashion, which makes it difficult to agree on. Going back to the example of a person who believes that the Earth is flat, they might construct an argument to support this position based on what they see as reason, while their belief might be largely influenced by their background (for example, religion).

None of these three methods could lead to long-lasting beliefs. According to

Pierce, only the scientific method is an acceptable tool for this purpose. In other words, we should rely on the empirical observations of the external world. Truth does exist, and it is an empirically supported opinion agreed upon collectively. Even if we do not know what hidden truths we will discover as we continue our inquiry, Pierce believed that the scientific process would eventually lead us there.

Why are beliefs important to Pierce? In his view, beliefs are formed through thinking as a result of the irritation of doubt and, in turn, form our habits. In other words, our thoughts serve to produce context-specific habits of action. The purpose of action is to produce a result. Hence, all thoughts at the root have something practical and tangible.

As Pierce put it in How to make our ideas clear (1878, part II, paragraphs 7 and 9),

There is no distinction of meaning so fine as to consist in anything but a possible

difference of practice… Our idea of anything is our idea of its sensible effects.

The concept of clear ideas was directed at fixing complex and often incomprehensible writing of metaphysics. According to Pierce, achieving clear ideas requires considering their practical effects, or evaluating a belief through the lens of its practical consequence. Based on this idea, Pierce formulated a pragmatic maxim in one of his most famous works How to make our ideas clear (ibid, part II, paragraph 9):

Consider what effects, which might conceivably have practical bearings, we

conceive the object of our conception to have. Then the whole of our conception

of those effects is the whole of our conception of the object.

Differences in meanings are linked to the differences they make in practice. To illustrate this idea, let’s go back to the part of this manuscript in which I differentiated between virtual worlds in general and VR in particular, and serious games and edutainment; in the former case, research findings on VR might not necessarily transfer to all virtual worlds due to unique hardware and software constraints imposed by VR technology. As for the latter, lumping narrow-focused, drill-based and largely unsuccessful games together with games that pursue larger social impact may lead to erroneous conclusions about their mechanics, potential benefits and use. Thus, in the context of the current work, evaluating the practical consequences of operating different definitions of virtual worlds, VR, and games justifies the proposed distinctions of meaning.

The pragmatism of William James

William James agreed with Pierce in that belief is linked to action. To illustrate his take on pragmatism in The will to believe (1897), James is telling a story about a dispute concerning a squirrel, an observer and a tree. Imagine a squirrel and an observer moving around the tree. Does the observer go around the squirrel in this situation? James argued that the answer depends on the practical definition of ‘going around the squirrel’. Both sides arguing for different answers can be wrong or right depending on the established context. The pragmatic method, thus, is useful for settling metaphysical disputes. If no practical difference can be made between notions, from the pragmatic perspective they

59 are the same thing. This idea is evident in the term “pragmatism” itself that comes from the Greek word “pragma” meaning “action, deed”.

James describes the pragmatic approach as follows (1906, paragraph 11):

A pragmatist turns his back resolutely and once for all upon a lot of inveterate

habits dear to professional philosophers. He turns away from abstraction and

insufficiency, from verbal solutions, from bad a priori reasons, from fixed

principles, closed systems, and pretended absolutes and origins. He turns towards

concreteness and adequacy, towards facts, towards action and towards power.

That means the empiricist temper regnant and the rationalist temper sincerely

given up. It means the open air and possibilities of nature, as against dogma,

artificiality, and the pretence of finality in truth.

It is important to note that James viewed pragmatism only as a method and not as a doctrine or dogma. From this perspective, theories become instrumental. They do not provide answers to all of our questions; rather, they serve as a corridor leading to different rooms. Each room might have a different outcome but the corridor is a necessary pathway to get to the rooms. The Chicago school of thought (including John

Dewey) believed that any idea is true instrumentally as long as it links parts of our experience together.

Pierce’s understanding of pragmatism was largely positivist; for him, observed consequences had to be observable by us. James (1906) expanded this idea to include psychological consequences. For James, both rationalism and empiricism fail to address human needs. Philosophy should address human experience by combining solid facts

60 from natural science with moral convictions and religious belief - made possible through pragmatism. James did not want to do away with metaphysics completely; instead, he aimed to analyze its questions by measuring what consequences they can serve. In part,

James was trying to reconcile religious beliefs with pragmatism by claiming that sometimes we can believe in something even if we do not have sufficient evidence to prove its existence. Intellect is only one part of forming beliefs; we should consider the psychological, emotional factors as well, including hopes, fears, and passions.

The philosophy of John Dewey

John Dewey and pragmatism

One of the central themes in the philosophy of John Dewey is the rejection of the concept of body-mind and subject-object dualism (Dewey 1920; 1929b). He traced its roots back to the times when humanity was not equipped to deal with uncertainties of life (such as hunger, natural disasters, diseases, etc.). People developed supernatural explanations to account for causes of such events, which gave rise to mythologies, religions, and moral codes. Art became another tool for controlling uncertainty by altering the environment.

The world, however, was still uncertain, which led to the divergence of beliefs along two dimensions. The first one was the empirical world with all of its uncertainty, unpredictability, and changeability. The other one was the supernatural, eternal world of perfection. The former was delegated to the working class while the latter became the privilege of the nobility. This divergence elevated the spirit over practical. Eventually,

61 when science and philosophy emerged, they were in the state of epistemological dualism.

Science was assigned to tackling messy, practical issues and philosophy was delegated the problems of the eternal and changeless.

For Dewey, this dualism was a problem. He argued that philosophy should become experimentalist and act as a method (Dewey, 1917, p. 30):

Philosophy recovers itself when it ceases to be a device for dealing with the

problems of philosophers, and becomes a method, cultivated by philosophers, for

dealing with the problem of men.

Along the lines of pragmatism of James and Pierce, Dewey claimed that our beliefs and ideas are tools that should be evaluated based on the consequences they produce (Dewey, 1920a, p. 145):

Conceptions, theories, and systems of thought are always open to development

through use… They are tools. As in the case of all tools, their value resides not in

themselves but in their capacity to work shown in the consequences of their use.

Dewey was an empiricist but his definition of experience differed from that of

James and Pierce. James was concerned with reconciling religious beliefs with pragmatism, and Pierce accepted only directly observable phenomena as the foundation of experience. Dewey was closer to James in that he did recognize the importance of the psychological and unobservable but he focused more on the biological and experimental approach to human life (Bacon, 2012). Much like Pierce, Dewey challenged the quest for

62 certainty, rejected the notion of absolute principles and beliefs, and posited that inquiry and the method of science are essential to everyday life and problem-solving. When we encounter an indeterminate situation, we aim at arriving at a determinate situation by adopting and testing a hypothesis (Dewey, 1938b). Should it prove successful, we reach what Dewey referred to as warranted assertability, which, in simple terms, means suspending doubt until new evidence challenges the current belief. The method of science, thus, is contingent on the consideration of all statements provisional until they are tested and proven to have warranted assertability, and being prepared to revise or reject certain statements or beliefs if new evidence points at their inadequacy. The normative authority over warranted assertability lies in the community of inquiry. To

Pierce, it is the community of scientists. Dewey believed that the entire humanity should adopt the method of science in everyday thinking. This had a large impact on his philosophy of education.

Dewey’s philosophy of education: What counts as educative experience?

The concept of experience is central to Dewey’s educational philosophy. Experience in

Dewey’s terms is different from its traditional definitions in empiricism. It is not passive and it is always in connection with the self who is undergoing the experience. It is through experience that learning happens, and each experience becomes a foundation of future experiences (Dewey, 1938a).

Not all experiences are created equal, however. They can be immediately pleasant or unpleasant and can have differential impacts on future experiences. Some of them might not be educative in that they do not foster the students’ desire to engage with other similar types of experiences. For example, constantly failing VS tasks in a high school or college course might lead to frustration and/or boredom with these activities as an immediate outcome, but also discourage students from trying to solve such problems in the future. Students might choose to go into a field that does not require the use of these types of skills or avoid similar tasks. Such experiences are not educative. They do not foster the students’ desire to keep learning throughout life, which Dewey saw as one of the most important outcomes of education (Dewey, 1938a).

Students’ experiences are continuous - they build on each other - but they are also shaped by the interaction between the external (Dewey calls them “objective”) factors and internal factors (Dewey, 1938a). External factors include the setup of the learning environment, including teaching practices, classroom arrangement, learning materials, and so on. Students’ prior experiences and background constitute internal factors.

Learning happens at the intersection of the two; their interaction determines how students respond to teaching practices, what type of experience they will go through and how their experience will relate to and influence their future experiences. This is in line with the triadic reciprocation model proposed by Bandura (reviewed previously): external factors are the environment, internal factors are cognition, and students’ behavior depends on their interaction and simultaneously shapes them too.

This is why students struggling with VS skills taking a class designed to improve this skill can respond very differently to the same instructor and make different choices about their future experiences. Those who have at least some educative experiences encouraging them to keep learning in the face of adversity might overcome the problems they are facing and improve significantly. Others, drawing upon their lack of positive educative experiences, might face a formidable impasse deterring them from productive action.

Dewey (1938a) proposed evaluating experiences as (non)educative based on the following questions. First, does the proposed problem reside within students’ personal experience and interest? In the same vein, could this problem arise naturally and would it be of interest and conducive to observation and experimentation outside of school? Or is this problem only used to teach specific information on a specific topic? When the answer is “no”, the resultant experience tends to be out of touch with the real world and is imposed externally. Giving a student a worksheet with abstract VS problems to be turned in by the end of the class is a telling example. This activity is not likely to lead to future inquiries and critical reflection.

The second question is whether the problem has the intrinsic, student-centered origin or is extrinsically imposed by a teacher or a textbook. Where does the locus of the problem lie? Again, a worksheet supplied by the teacher is not likely to have any relation to what the student is interested in or cares about. Such experience, then, will teach the student that worksheets need to be finished and turned in when the class time is up. Will

65 it help them learn the problem-solving processes underlying much of professional work in any field? Will they keep working on these or similar problems when they get home because they are emotionally and intellectually invested in these problems?

This brief analysis leaves us with important considerations in crafting educative experiences. An effective VS curriculum should be grounded in a practical context and student-initiated problems that are relevant and interesting to them. The desired result is not to cross the content items off the list but to facilitate students’ desire to keep learning in and out of formal educational settings.

Three pillars of educative experiences: Communication, collaboration and problem- solving

Any type of experience belongs to some kind of context – it does not happen in a vacuum. Experiences unfold as part of socio-cultural practices and involve communication. Communication is both the means and the end (Dewey, 1929a). It is a means of transferring information and how we construct shared meanings.

Communication, therefore, assumes a crucial role in learning and the everyday life of a democratic society.

Dewey believed that schools should be part of the larger democratic processes; schools are places to practice democracy. He defined democracy as a state of affairs in which people can determine the conditions and goals of their work and conduct the

66 activities together with a larger community (Dewey, 1903). Democracy is not just a form of government; “it is primarily a mode of associated living, of conjoint communicated experience” (Dewey, 1916, p. 54). People in a democratic community develop shared interests and allow free-flowing communication between smaller groups within societies.

Through the ongoing communication and negotiation, these groups develop their shared ends in view and engage in the problem-solving process to address the problems the society faces. The ends in view might change over time, and each problem is unique and requires a collaborative effort to address it. Collaborative problem-solving, thus, becomes a cornerstone of a healthy democratic community.

A democratic society requires its members to be educated to carry the democratic conditions forward and to better the social conditions of humanity. It “must have a type of education which gives individuals a personal interest in social relationships and control, and the habits of mind which secure social changes without introducing disorder”

(Dewey, 1916, p. 60). Such education serves a social purpose. It is a way for learners to develop new habits of mind to practice democracy in everyday life.

We can now add another criterion of experience to further refine our foundations of the intervention - collaborative problem-solving. Problem-solving in traditional education is often a solitary endeavor. For the fear of cheating and for the sake of testing individual content knowledge, students are required to work independently. Quantifying individual knowledge with standardized measures becomes a more important outcome than allowing students to engage in democratic practices in school settings. And yet, the

67 professional world, especially in the STEM area, heavily relies on collaboration.

Problem-solving is done best as a collective action bringing together opinions and perspectives of several, sometimes dozens of people, each of them having something valuable to contribute to the problem-solving process. Outside of formal educational settings, the groups in which students are social participants become their new context of learning. Learning through collaborative-problem solving is then practicing democratic engagement that is essential to one’s professional and civic functioning.

The idea of collaborative learning is not new (Dillenbourg, 1999; Dillenbourg,

Baker, Blaye, & O’Malley, 1996; Johnson & Johnson, 1987; Nelson, 1994). However, it is usually framed from the perspective of motivational, affective and cognitive benefits.

Collaboration is useful because students can capitalize on skills and resources from multiple people; it makes students more motivated to actively learn from their peers; it develops conceptual knowledge and interpersonal competence and promotes the consideration of different perspectives and views (Lai, 2011; Van Boxtel, Van der

Linden, & Kanselaar, 2000).

However, collaborative problem-solving from this perspective is still a learning tool whose purpose is mostly to make students more inclined to learn the content.

Students do not work toward developing a shared meaning; they work toward discovering the content that the teacher sets up for them. To allow students to practice democratic problem-solving in classroom settings, collaboration should occur authentically - as an attempt to create a shared group meaning as a way to address relevant problems that the

68 community faces. Such collaboration is likely to happen in groups that have a strong sense of collective efficacy (Bandura, 2000). Similar to self-efficacy, collective efficacy can be defined as a belief in being able to complete a task – with one exception: this belief reflects the groups’ confidence in group success as opposed to their confidence in their individual ability to succeed. High collective efficacy leads to more political activism and engagement in civic discourse (Bandura, 1997). When students practice building close-knit learning communities with high levels of collective efficacy, they practice the democratic discourse, which shapes their perception, attitude and skill sets related to engaging in such discourse in the future.

In the framework of the current study, I will use philosophy the way Dewey intended it to be: as a method to solve an educational problem. Now that we have established essential principles of creating truly educative experiences based on Dewey’s ideas, the question is: How can we implement them in an intervention that serves to develop visuospatial self-efficacy and skills? I believe games can serve as a bridge between the two as they can be considered Deweyan worlds in the sense of how they engage players. In the next section, I explain this idea in more detail.

Games as Deweyan worlds

Play and Work

Dewey did not live long enough to witness the mass spread of games and virtual worlds technology, permeating our daily life and starting to make its way into formal education. 69

Relatively little research has been dedicated to connecting the use of technology in education to Dewey’s ideas and even less grounding Deweyan ideas within the context of video games (e.g., Bannen, 2018; Hickman, 2009; Hickman, 2017; Waddington, 2015).

However, upon closer examination of educational principles proposed by Dewey, games seem to have a great potential to provide a fruitful platform for their implementation.

As Dewey pointed out in Democracy and Education (1916), educators seem to avoid play as part of an educational process as play is often not seen as a viable means of learning; why focus on play in school when students do it in informal settings anyway?

Dewey argued that play is as essential in education as work, and that the two concepts, in fact, are not that different (Table 2). Both imply ends-in-view and require adapting the materials, resources, and means to achieve those ends-in-view. What sets them apart is the nature of the ends-in-views. When we play, our end-in-view is usually to continue playing; that is, we focus on producing a subsequent action that will lead us to further actions involved in the act of play. When playing Tetris, we rotate and place objects to fit the existing patterns we created with a sole purpose of doing it for as long as we can as we are motivated by more direct interest and engagement. The same is true for more complex games. In World of Warcraft, players engage in raids and quests to achieve proximal goals but ultimately their end-in-view is to continue playing and exploring. In work, the end-in-view is often more remote, involves a change in conditions or a specific situation, and requires more planning and persistent effort to achieve the end-in-view - partially due to the more long-term and specific nature of the ends-in-view.

Table 2. The Differences and Similarities of Work and Play.

Play Work Have ends-in-view (anticipated results) Similarities Hinge on the adaptation of materials and means to achieve ends-in-view More direct interest - Focus on producing a Focus on producing a change in the subsequent action state of things Differences No necessity to look far ahead Usually requires anticipation of remote results Flexible, easy to alter on the fly Requires more planning and persistent effort

That is not to say that games are conducive to facilitating the process of play only.

In some cases, players can (and are encouraged to) set distal goals with a specific plan of action in mind and exert substantial effort to achieve their aims. For example, in a role- playing (RPG) game Dragon’s Dogma: Dark Arisen, some players set a goal of completing every available quest or reaching the highest possible level, which requires careful planning, strategizing, additional research and sometimes collaboration with the game community.

To Dewey, play should be integral to education and serve as a foundation that leads to work through a natural progression. Work, however, is defined in a very specific way. Dewey differentiates between work as a deliberate process of attaining one’s ends- in-view while appreciating the value of these aims and labor or drudgery as extrinsically assigned tasks (Dewey, 1916). Activities that follow predetermined procedures and rules or are rooted in mindless replication of existing models do not require establishing ends- in-views, selection and adaptation of means. They are akin to slave or factory labor,

71 which are dictated by the owners and the economy and do not take into account individual aims. Work has intrinsic continuity - it is not a progression of separate acts

(which is often the case in the drill and test-based learning activities). Work is directed by aims that are formed by individuals engaged in it. Education in and of itself cannot have aims; only people do. Aims should be based upon observing existing conditions, interpreting current resources and difficulties and taking into account one’s current experiences.

This is one of the reasons why edutainment largely failed; instead of providing students with a meaningful ground for play and work, their creators sugar-coated labor under the guise of gamification. In such games, exploration of the environment and engaging in a meaningful, self-directed problem-solving process gives way to persuading students into performing separate acts of labor (for example, solving math problems) superficially connected by a storyline or sequence of events. From a Deweyan perspective, such games are no different from asking students to fill out worksheets with math problems.

How do video games embody Deweyan principles of education?

Video games embody many principles that correspond to Deweyan ideas of democratic education. By nature, digital games are virtual worlds inviting discovery and exploration.

They are similar to hands-on materials whose lack in the classroom Dewey lamented in

Democracy and Education (1916). In the absence of materials and occupations, students cannot encounter and generate meaningful problems. They work with problems that they

72 have as students, not as human beings, and the predominant concern becomes adhering to the teacher’s requirements. For example, students in an undergraduate-level STEM course might be concerned more with formatting their homework assignments and memorizing their instructor’s lectures word by word to regurgitate the information back to receive a high grade as opposed to experiencing and learning from the phenomena that are at the heart of the course.

Games could at least partially address Dewey’s concern about the lack of materials in the classroom. As he writes in Democracy and Education (1916, p. 94),

Where schools are equipped with laboratories, shops, and gardens, where

dramatization, plays, and games are freely used, opportunities exist for

reproducing situations of life, and for acquiring and applying information and

ideas in the carrying forward of progressive experiences.

Games are often fairly complex systems that can represent any possible environment, from real laboratories and gardens to outer space and imaginary fantasy worlds. They also embody Dewey’s ideas of giving students something to do, not to learn, in a way that demands thinking and making connections. Learning is intrinsic to game ecosystems; you cannot start playing a game if you do not learn at least the basic controls, and many games feature complex challenges that take dozens of hours of learning. The difference between learning in games and traditional education is that in the former it always happens through action. League of Legends, a Multiplayer Online Battle

Arena (MOBA) game, for example, requires learning about essential game mechanics

(including types of characters and abilities, weapons, movements, and attacks), specific

73 strategies (how to counter certain characters’ abilities, when to storm and destroy a tower and when to fall back), communication and collaboration skills (how to support team members and communicate your intentions). All of these skills are acquired through play.

Reading about one’s character abilities does little to translate these abilities into effective and strategic gameplay. Many players engage in high-level reflection on current most popular characters, weapons, and strategies of play and construct theories of what is known as the current ‘meta-game’ - most optimal ways of playing the game at the moment.

Based on the survey of Dewey’s works presented in the previous section, I summarized 8 salient principles underlying educative experiences. They are displayed in

Table 3 together with the corresponding characteristics of game worlds that embody these principles and specific examples from video games.

Table 3. The Correspondence of Deweyan Principles of Education and Game World Design.

Deweyan principle Game characteristics Examples

The continuity of Consistency of • Consistent move set of similar experience gameplay and weapon types in Dark Souls III. mechanics • Dragon’s Dogma: Dark Arisen: a continuous progression of quests to obtain the heart stolen by the dragon. • Intentional inconsistencies in fighting large enemies in Dark Souls III. Continued

Table 3 Continued Organic nature of Games present • Dragon’s Dogma: Dark Arisen: The problems problems to players Hydra slaying quest is motivated by within the existing previous plot events. lore, plot and story • Tomb Raider series: going through a progression series of obstacles because the short and easy route is blocked.

Intrinsically Games allow players • Minecraft: players can choose motivated choice of to set their own various styles of play. problems and aims goals, especially in open-ended worlds

Fostering motivation Games use intrinsic • Dragon’s Dogma: Dark Arisen and to keep learning and extrinsic Monster Hunter: accruing resources motivators and improving gear. • League of Legends: praise and social recognition. • Dark Souls series: seeing improvements in fighting skills and exploring the lore.

Learning through Games present • Dragon’s Dogma: Dark Arisen and problem-solving problems for players Monster Hunter: completing specific to solve quests or figuring out effective gears, items, and strategies.

Collaboration Many modern games • League of Legends: players work in include collaborative teams to capture the enemy’s base. gameplay • Monster Hunter: players can create quests for other players to join. • Dark Souls III: players can leave messages with tips and tricks and summon other players. • Dragon’s Dogma: Dark Arisen: indirect collaboration through hiring other players’ pawns.

Continued

Table 3 Continued Thinking as a Games allow to • Portal 2: unlimited number of tries method of intelligent experiment and test to test different mechanics to solve experience and hypotheses through puzzles. inquiry instant feedback and • Tomb Raider series and Zelda: interactivity Breath of the Wild: save files and keeping one’s equipment to encourage experimentation and mistakes. • Dark Souls III: trying different fighting strategies.

The role of Games are often • World of Warcraft and League of community catalysts for large Legends: large online communities communities sharing advice, tips, and news.

Principle #1: Experience is continuous: present experience builds on previous experience and affects future experience.

Games embody this principle through the consistency of gameplay and mechanics. Players are guided through various experiences that (at least in well-designed games) form certain expectations of how the game environment responds to players’ actions. Inconsistencies in this behavior are used for further learning or as an intentional element of surprise.

For example, within a given weapon class in Dark Souls III, the player can expect weapons of similar types to have similar move sets and weapons of different sizes and weights differ in speed, stagger power, and damage (Figure 13).

Figure 13. Some Weapon Categories in Dark Souls III. The plot in Dragon’s Dogma: Dark Arisen starts when a dragon steals the player’s heart (quite literally) and all events consistently lead to seeking the dragon out to get the heart back. Interestingly, Dragon’s Dogma also seemingly breaks this continuity at the very start of the game in which the player gets to try the main mechanics as a different character engaging in battle with little to none explanation as to who the character is and why it matters. Another example of an intentional inconsistency in Dark Souls III is certain boss fight mechanics. Many enemies are large in size and the default course of actions the player might assume after similar encounters would be attacking the lower part of the body. However, certain large enemies are immune to this type of damage and require players to figure out other weak spots to hit. The more players engage in fights, the wider their repertoire of fighting strategies and the more varied their expectations.

Principle #2: Natural, organic origin of problems rooted in the current conditions.

Work in a Deweyan sense is rooted in meaningful problem-solving, wherein problems arise naturally from students’ experience. Games serve as an organic context

77 for discovering and solving problems. It could be argued that every action in games serves to resolve an issue at hand. What makes games unique in this respect is the organic presentation of problems within the context of existing lore, plot and story progression.

Even though some problems are technically offered to the player by the game creators, when players become immersed in play, they treat such problems as a natural part of the playing process.

When players In Dragon’s Dogma: Dark Arisen face a formidable Hydra monster and are tasked to defend the encampment, this event is a continuation of their journey set in the context of the unfolding story. This fight seems like a natural continuation of past events as opposed to an imposed task. At the same time, role-playing games (RPGs) are infamous for “collect N number of X” types of quests that many players perceive as drudgery, so the implementation of this principle is largely dependent on good game design. In many platformer and adventure games, the problems that the player has to solve are related to obstacles preventing players from achieving goals. In the Tomb

Raider series, for example, players often have to take a dangerous route full of enemies, traps, and puzzles because the short and safe route is blocked.

Principle #3. Intrinsically motivated choice of problems and aims.

Truly educative experiences hinge on problems that are relevant to students’ experience and ends-in-view. Many games give players the autonomy to set their own goals, especially in open-ended worlds. These goals can include the exploration of the world (sometimes at the expense of the questline), item collection, weapon upgrades, reaching the top position on a leaderboard, helping new players, providing support to

78 others in the community, and so on. Some games allow more autonomy than others;

Minecraft, for example, gives players the freedom to choose what they want to do in general. It can be survival, building, creative expression, and more (Figure 14). Games like Angry Birds are more restrictive in that the goal of the activity is strictly defined

(pass the level by getting rid of all enemies); but even then, the player always has some level of control over how they approach the game.

Figure 14. Minecraft. Principle #4. Fostering motivation to keep learning.

According to Dewey, education should, first and foremost, motivate learners to keep learning. Games are exceptionally good at doing that through the use of both intrinsic and extrinsic means. Extrinsic rewards are typically represented as scores, levels, 79 achievements, social recognition, and unlocked content. Intrinsic means include providing constant and clear feedback about the player's progression and growth of skills.

In RPG games like Dragons’ Dogma and Monster Hunter (Figure 15), accruing resources and improving gear is often a big motivator. In League of Legends, praise and social recognition from other players often encourages players to keep honing their skills. In the

Dark Souls series, many players are motivated by seeing improvements in their fighting skills, especially when they go back to the previous locations. Some players are also motivated to explore the game to learn about the lore of the game universe.

Figure 15. Monster Hunter: An Example of Acquired Resources. One of the crucial factors in sustaining players' motivation is offering appropriate or adjustable levels of challenge. As Dewey puts it (1916, p. 91),

A difficulty is an indispensable stimulus to thinking, but not all difficulties call out

thinking. Sometimes they overwhelm and submerge and discourage. The

perplexing situation must be sufficiently like situations which have already been

dealt with so that pupils will have some control of the meanings of handling it. A

large part of the art of instruction lies in making the difficulty of new problems

large enough to challenge thought, and small enough so that, in addition to the

confusion naturally attending the novel elements, there shall be luminous familiar

spots from which helpful suggestions may spring.

Games are often balanced to make sure that the level of challenge presented to the player is adequate but not overwhelming, and many games allow a lot of autonomy in adjusting the level of difficulty. For example, Dragon’s Dogma: Dark Arisen offers three levels of difficulty: easy, normal and hard, each affecting the amount of health, stamina and received damage. This system is common across games. Dark Souls III, on the other hand, does not offer differential levels of difficulty, which, in part, made it famous for being very challenging. At the same time, Dark Souls III balances it by giving the player opportunities to regain the souls lost upon death (the main in-game currency) and to keep acquired items intact, to run past most enemies, to carry many different supporting items that do not count against the equip load (maximum weight of items equipped) and to summon other players for help.

Principle #5: Learning through problem-solving.

Problem-solving is a natural part of human life. Dewey argued that problem- solving skills are essential for advancing the human condition and sustaining democracy.

In games, players usually progress through a series of problems; problem-solving

81 becomes the main instrument of learning about the underlying system and developing relevant skills.

Most actions players take in games are to address some kind of problem. In RPGs like Dragon’s Dogma and Monster Hunter, problem-solving often involves completing specific quests but it can also be related to figuring out which gear is most efficient against specific enemies and what items can be combined to create new powerful unique items.

Figure 16. Dragon’s Dogma: Dark Arisen Quest Example. Dewey emphasized the importance of exploration of potential solutions in problem-solving as opposed to providing students with ready-made solutions. As he noted in Democracy and Education (p. 93),

It is that no thought, no idea, can possibly be conveyed as an idea from one

person to another. When it is told, it is, to the one to whom it is told, another

given fact, not an idea. The communication may stimulate the other person to 82

realize the question for himself and to think out a like idea, or it may smother his

intellectual interest and suppress his dawning effort at thought. But what he

directly gets cannot be an idea. Only by wrestling with the conditions of the

problem at first hand, seeking and finding his own way out, does he think.

Games often allow players to solve problems through a variety of means. For example, in Dragon’s Dogma players can choose to engage enemies face-to-face, hide behind an obstacle, hire powerful pawns and come back, or simply run away and pass on the challenge. In League of Legends, a variety of strategies can be employed to achieve the goal of capturing the enemy base, and many esports teams secure victories by using the game mechanics in unexpected and creative ways.

Principle #6: Collaboration.

To Dewey, learning is inherently social. It is through communication and social interactions that we negotiate shared meanings and form shared ends-in-view. Problem- solving is most optimal in a distributed, open environment with a free exchange of ideas.

The proverb “two heads are better than one” is a good illustration of this approach; several people, provided they engage in egalitarian communication, have a better chance to come up with a creative solution than one expert (Dewey, 1916).

Many modern games lean heavily toward collaborative gameplay. In part, it was made possible by the advancement of the Internet and relatively accessible powerful personal computers. Local co-op games and online multiplayer games are now established genres. Collaboration in games can be implemented very differently. In

League of Legends, several people usually work together to capture the base of the other

83 team. In Monster Hunter, players can post quests inviting other players to join. In Dark

Souls III, players can leave messages with tips, tricks, and warnings and summon other players to help them (Figure 17). In Dragon’s Dogma, collaboration is indirect through hiring pawns of other players and improving their knowledge of enemies and areas as well as gift-giving. Regardless of its method of implementation, the gaming industry recognized the social nature of gaming and most modern games tend to offer some version of collaboration or, at the very least, integrated options to share player’s progress with other players (e.g., by posting screenshots and playthrough videos on social media).

Figure 17. Dark Souls III: A Message from Another Player.

Principle #7: Thinking as a method of intelligent experience and inquiry.

Educative experience, according to Dewey, involves using thinking as a method to conduct inquiry to test hypothesized results of a particular course of action (Dewey,

1916). Games are natural sandboxes (even when players’ actions are limited). Their interactivity and instant feedback allow experimentation and testing one’s hypotheses about certain mechanics, events or actions and adjust one’s course of action accordingly

Portal 2 allows an unlimited number of tries to test different mechanics to solve puzzles (in Figure 18, notice the room setup: whatever the player tries to do, they can always come back to the starting point and try again). Most of the Tomb Raider games and Zelda: Breath of the Wild have supporting systems in place to help players when they fail or make mistakes, such as save files, not losing one’s equipment, etc. In Dark Souls

III, inquiry takes the form of trying different fighting strategies against enemies. More importantly, games often provide space for reflection on the results of players’ experiments. These results usually have an impact on how they engage with the game, and after multiple unsuccessful attempts to solve a problem, players can experience an

‘aha!’ moment, which happens through their reflection on the consequences of their actions.

Dewey also emphasized the crucial importance of mistakes as learning opportunities. When teachers select materials that do not allow the possibility of making mistakes, it “restricts initiative, reduces judgment to a minimum, and compels the use of methods which are so remote from the complex situations of life that the power gained is of little availability” (Dewey, 1916, p. 112).

Figure 18. Portal 2: An Example of a Puzzle. Games often encourage failure by incorporating various mechanics. As mentioned before, continuously saving players’ progress is one of them. Most often, even when failure has consequences, they are not very critical. For example, in Dragon’s Dogma:

Dark Arisen, most of the used items are recoverable (i.e., can be purchased or found again) if the player decides to fall back, and the player does not lose anything if their character dies.

Principle #8: The role of community.

Dewey saw schools as an integral part of the society in which students can engage in democratic social processes without the imposed constraints of the economy.

Education takes place in communities and serves them.

Games often serve as a catalyst for creating large communities in which players continue to engage in collaborative problem-solving even outside of the game world. 86

World of Warcraft and League of Legends have online communities of hundreds of thousands of people. On online forums (both on official platforms and social media such as Reddit and Facebook), players debate the best builds (i.e., set of equipment, weapons, spells, etc.), current meta-game, share news about esports events, and help each other figure out game-related problems. Sometimes community members serve as social regulators within and outside of game systems. In League of Legends, players can report other players when they violate the accepted code of conduct, and in the past, such violations were critically checked and assessed by volunteers from the community before making a decision about banning the violator.

Overall, many design elements in video games align with the Deweyan principles of democratic education. In other words, well-designed game worlds can become

Deweyan worlds for teaching and learning. In the next chapter, I show how Deweyan principles can be implemented to create a serious game for visuospatial skill training and the potential impact of such a game.

Figure 19. World of Warcraft Forum.

Summary

One of the main questions driving the current study is: How do we create educative mastery experience to learn VS skills? To answer this question, I turned to pragmatism and the philosophy of John Dewey whose work largely focused on the nature and role of experience in education. Within the pragmatic framework, inquiry serves to provide tools to cope with the world as opposed to reaching absolute truth. Absolute truth is rejected as a concept; truth becomes contextual. Dewey preferred the term ‘warranted assertability’, emphasizing the fluidity of knowledge and the process of inquiry and the fact that we should be ready to revise our beliefs once we encounter compelling evidence challenging our current view.

Dewey’s educational philosophy was grounded in the pragmatic framework and underscored the importance of inquiry and method of science, collaborative problem- solving, and the role of experience. Many of these principles are embodied in video game design; well-designed video games can become Deweyan worlds. I synthesized 8 principles of creating educative experiences rooted in the philosophy of John Dewey and demonstrated how they are embodied in video games. They are as follows:

1. The continuity of experience (consistency of gameplay and mechanics);

2. Organic nature of problems (presenting problems to players within the existing

lore, plot and story progression);

3. Intrinsically motivated choice of problems and aims (allowing players to set their

own goals, especially in open-ended worlds);

4. Fostering motivation to keep learning (intrinsic and extrinsic motivators);

5. Learning through problem-solving (presenting problems for players to solve)

6. Collaboration (collaborative gameplay);

7. Thinking as a method of intelligent experience and inquiry (allowing

experimentation and hypothesis testing through instant feedback and

interactivity);

8. The role of community (games serve as catalysts for large communities).

The present study, its significance and research questions

The present study sought to create, implement and assess a game-based visuospatial intervention for middle school students. For the purposes of the intervention, I developed a desktop/VR collaborative game called Bond grounded in the pedagogical principles based on the self-efficacy theory and the educational philosophy of John Dewey. The study followed a quasi-experimental design and was implemented in 3 middle schools and 11 classrooms. Students were divided into two conditions: experimental (engaged in the intervention for 2 weeks) and control (business-as-usual, no intervention). The development of several outcomes was of interest, including visuospatial self-efficacy, visuospatial performance, and STEM achievement, controlling for several covariates.

The study contributes to the existing body of research in several ways. First, it connects the field of game design and educational philosophy and psychology by linking educational principles proposed by John Dewey with game design principles, demonstrating how games can be used as Deweyan worlds. Second, it provides a detailed account of the design process of a serious game rooted in the abovementioned principles, providing practical guidance in creating pedagogically sound educational games. Third, it conceptualizes and provides empirical support for the concept of visuospatial self- efficacy, bridging the gap in the literature and proposing a new approach to visuospatial interventions. Finally, it investigates the relationship between the proposed concept of visuospatial self-efficacy, visuospatial performance, and STEM achievement.

The current study addressed four questions. One of them was philosophical and methodological in nature:

Design Question: What processes and principles should guide the development of a serious game based on pedagogical foundations and the principles of game design?

This question was answered through the reflection on the iterative and continuous process of designing the serious game for the study, building on philosophical and educational principles discussed above.

The other three questions were empirical and were answered through the quasi- experimental design of the study. These questions explored the effectiveness of the intervention and the relationships between the variables of interest:

Research Question 1: Did the game-based intervention improve students’ VS self-efficacy?

Hypothesis: The intervention will improve students’ VS self-efficacy.

Research Question 2:

Did the game-based intervention improve students’ VS performance?

Hypothesis: The intervention will improve students’ VS performance.

Research Question 3: Did the game-based intervention improve students’ STEM performance?

Hypothesis: The intervention will improve students’ STEM performance.

Chapter 3. Intervention design

The design and development of the game

Essential principles of game design

What is a game?

Game design is a complex problem-solving endeavor that draws on many different fields, including psychology, computer science, sociology, graphic design, and more. Game design principles are contingent on considerations of many factors, and one of the first questions to answer in the process is: what is a game?

Many definitions have been proposed in the literature (see chapter 7 in Salen,

Tekinbaş, & Zimmerman, 2004) with various degrees of complexity but probably the most concise definition was offered by Schell (2015, p. 47) who defined games as “a problem-solving activity, approached with a playful attitude.” Schell summarized some common elements shared by other definitions of games, resulting in a list of 10 qualities that are typically found in games (p. 44):

1. Games are entered willfully. Players play games because they want to, not because they are told to.

2. Games have goals. Players always strive to do something in the game world.

3. Games have conflict. It can be a conflict between the player as a protagonist and an antagonist (or antagonists), between players, or between the game and the player

(e. g., avoiding defeat in Tetris for as long as possible).

4. Games have rules. This is one of the most foundational aspects of games. Rules are the glue that holds the game system together. Remove the rules of soccer or basketball, and the entire activity turns into random physical movements with no goal or conflict.

5. Games can be won and lost. This is true for many games but not for all. For example, Tetris does not have a definite win state; only the lose state is defined. You can play for a long time but the only certain outcome is that eventually you will lose. In

Journey (produced by Thatgamecompany in 2012), on the other hand, players cannot lose; the game is designed as an emotional experience and an exploratory tale. At the same time, there is no win state either; game completion indicates the end of the experiential journey but neither the player nor the game lose or win anything. Therefore, we should keep in mind that this quality is common but not mandatory.

6. Games are interactive. Players can always interact with the game structure in one way or another. Sometimes the interactions are as simple as tapping on certain images on the phone screen, and sometimes they take the form of complex actions, from creative use of game controls to speedrunning the game (trying to complete the game as fast as possible capitalizing on certain game affordances and tricks and hacks).

7. Games have challenges. More importantly, good games have an appropriate level of challenge. Types of challenges vary. In Tetris, it is to keep up with the speed of

93 the shapes’ movement and to predict the most optimal positioning taking into account the current and future shapes. In Dark Souls III, challenges range from finding specific items in difficult locations to defeating powerful enemies to beating the game. In the

Civilization series, players are challenged to create an entire civilization which entails overcoming financial, military, social, and many other challenges.

8. Games can create their own internal value. This means that things that are valuable in the game are only valuable within this game. In World of Warcraft, players go on hour-long raids to obtain rare items (e. g., powerful weapons) that have no value in the real world but within the ecosystem of the game, their value is very high (including both monetary and social recognition value). Sometimes this internal value is so compelling that players trade items for real-world money.

9. Games engage players. In other words, players usually feel immersed in games.

10. Games are closed, formal systems. They include many elements that work together to create the experience. They are formal systems because they have rules. They are closed because they have boundaries with the real world that sometimes can sharply delineate our feelings, choices, and values in-game versus in the real world. For example, players who support peaceful conflict negotiations in real life might opt to use brute force in games (some games, such as shooters, are specifically geared toward using brute force; others, such as RPGs, give the player a choice to pick a certain style of play, which might include charismatic persuasion, deception, intimidation, ignoring, helping, attacking, and so on).

To sum up, games motivate players to engage in play; they have rules, goals

(predetermined or set by the player) and challenges, and are interactive. They often create compelling virtual worlds that have value to players within the game, and they often have elements of conflict (player vs the environment or player vs player) and win or lose states. In the development of the intervention game, I considered most of these elements, with a particular emphasis on what motivates the player, how the rules are defined and discussed, what goals are possible and how they can be achieved, and the levels and types of challenges. The practical implementation of these elements is discussed later in the manuscript.

Four constituent elements of games

According to Schell (2015), games can be broken down into four basic elements, including mechanics, story, aesthetics, and technology.

Mechanics are “the procedures and rules of your game” (p. 51). They define how players can engage with the game, what goals are possible, how players can achieve these goals, and what happens when players interact with the game and/or other players.

Mechanics are foundational to the choice of the story, aesthetics, and technology.

The story is the narrative of the game. It is usually conveyed through a sequence of events in the game and can be linear and non-actionable or interactive and impacted by the player’s choice. Stories can be told through dialogues, item description, cutscenes, the design of the environment, music, effects, and more. In Dark Souls III, much of the lore is told through items and dialogues with other characters, while in a fantasy table-top

Dungeon and Dragons game a lot of the story comes from the Dungeon Master, the leader of the game campaign, and players can craft and change the story on the fly through their character actions and choices.

Aesthetics refers to the feel of the game. Most of it is created through the visual and auditory aspects, including soundtracks, ambient music, sound effects, the style of graphics, visual effects, and so on. The title screen in Super Mario Odyssey appears on a bright red background accompanied by Mario’s cartoon voice and a light-hearted jazz- like soundtrack (Figure 20), while the title screen of Dark Souls III sets the mood with a dark background accompanied by an uneasy, eerie, dark, orchestra soundtrack (Figure

21). The player forms an expectation of what the game will be like from the very first seconds of engaging with the game.

Figure 20. Super Mario Odyssey Title Screen.

Figure 21. Dark Souls III Title Screen. Finally, technology refers to the medium that the game is presented through.

Certain mechanics, aesthetics, and story decisions determine the choice of technology.

The vast and complex world of Dark Souls III and its fighting mechanics cannot be adequately implemented on a mobile platform or in Virtual Reality but can be accommodated by console and PC systems, while games like Candy Crash in which players mostly need to change the positions of various elements can benefit from mobile interfaces.

All of these constituent elements were considered in great detail at the design stage of the game together with other defining characteristics of games discussed above.

Developing Bond

Design choices

The mechanics, aesthetics, story, and technology for the game created for this project were determined by the theoretical principles developed based on the philosophy of John

Dewey and the theory of self-efficacy. The principles and corresponding game design decisions are presented in Table 4.

Table 4. Game Design Decisions and the Underlying Theoretical Principles.

Theoretical Game design decisions principle

Deweyan The continuity All challenges are connected by the general theme principles of of experience and build on each other education Organic nature Players find themselves in confined spaces which of problems they are trying to escape; the challenges are natural ways of moving from room to room.

Intrinsically Although the goals of this game are predetermined motivated (solve the puzzles and get to the end of the game), the choice of aims means to do so are open-ended. Players can choose how they want to engage with the puzzles and each other.

Fostering No extrinsic motivators such as rewards or score; intrinsic solving the puzzles and discovering where the game motivation leads serve as motivators.

Collaborative The game requires at least two players to work problem-solving together. One player has clues to solving the puzzle, the other player solves it (the roles switch every challenge). The two players use different platforms that are not connected online, meaning they cannot see each other and need to use verbal communication to guide each other through the levels. One player cannot progress to the next level without the other. Continued

Table 4 Continued Inquiry and the The puzzles are designed to encourage both experiential method of learning (interacting with the world to see the results) thinking and critical problem-solving skills (thinking about a way to solve the challenges most effectively). There is no punishment for mistakes and no time limit except a few situations when the time limit adds another layer of challenge.

Community The players can communicate with others if they want.

Self- Mastery All puzzles are designed to challenge students’ efficacy experiences visuospatial thinking skills and engage in visuospatial thinking and problem-solving.

Vicarious experiences The collaborative nature of the game allows students to see their game partners as role models and verbally Verbal acknowledge each other’s success persuasion

Proximal goals Each level has only one challenge (except for Branch 4 and appropriate which includes 4 challenges in one level). The Practice levels of Branch serves as a gentle introduction for game controls difficulty and challenge mechanics and has a lot of explanations. Each branch features progressively difficult challenges.

The game was designed and developed specifically for the intervention by the author of this dissertation with the assistance of the author’s research team and a programmer with game development experience. I named it Bond to highlight the importance of creating bonds between the players as they engage in collaborative problem-solving.

The game is first-person, puzzle-based, and space-themed. The players find themselves in different enclosed spaces on a spaceship, and the only way to move forward is to solve puzzles. The game does not feature much of a story (the story will be

99 added in future releases) but the environment makes it clear that players are (a) in space;

(b) on a spaceship; (3) trying to find a way out.

Bond was designed to be a collaborative game by default, meaning a single player cannot complete the game; it is designed to be played on two devices at the same time by two (or more) players (see examples of gameplay on learnspatially.com). Furthermore, the game does not have online multiplayer functionality; the progress on Player 1 (P1) is not seen by Player 2 (P2) and vice versa, and they cannot communicate within the game.

Instead, verbal communication (either in the same physical space or through voice chat using other software) is the only way to advance through the levels.

The game was designed to be played on two different devices: a desktop computer and a smartphone with a mobile Virtual Reality headset. When using mobile

VR, the player is separated from the physical world, reinforcing the necessity to communicate with their partner to progress in the game. Both players thus need to develop a shared vocabulary to describe their surroundings, challenges and solutions, engage in constant communication and establish a common point of reference to solve challenges. Of course, nothing stops P2 from taking the headset off and looking at P1’s screen, or both players switching in the middle of solving a challenge thus being visually exposed to both environments. Even if players decide to look at each other’s screens, solving game challenges still requires constant interaction.

The challenges were designed to train visuospatial skills on different scales with different types of tasks. 7 types of challenges are present in the game; they are described in Table 5.

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Table 5. Challenges in Bond.

Rotating an object to match a certain Rotating an object to match its shadow to a orientation within a certain number of certain shadow orientation (“Shadow moves (“Rotation challenge”); challenge”);

Rotating an object to match a certain Jumping on platforms on a 4x4 field in a orientation of the colors on its faces specific sequence (“Jump field challenge”) (“Color orientation challenge”);

Navigating a maze (“Maze challenge”); Rotating tiles on a predefined path so that their pattern matches the pattern on the side of the cube that steps on these tiles (“Stepping cube challenge”)

Continued

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Table 5 Continued Navigating a field of paths in which one player sees the next path but the other doesn't (“Trust challenge”);

Each player has either a supporting or an active problem-solving role, and in some challenges both players solve the problem at the same time. Both players have some information about their current challenge but not enough to solve the challenge by themselves. For example, in the rotation challenge, P1 can rotate an object and sees the number of moves available but does not know the target orientation of the object. P2 sees the target orientation of the object but cannot move it. To solve the challenge, P2 needs to explain what the target orientation looks like and support P1 reasoning. The full list of the problem-solving processes involved in each challenge is presented in Table 6.

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Table 6. Game Challenges and the Problem-Solving Processes Involved.

Challenge Problem-solving processes type Rotation Partner 1: challenge • Describe the initial object orientation. • Visualize the final object orientation. • Analyze prominent parts of the object and their orientation. • Visualize which ways the object can be rotated within the given number of moves. • Communicate the steps they are taking to rotate the object and the result of each step.

Partner 2: • Describe the final object orientation. • Visualize the initial object orientation. • Analyze prominent parts of the object and their orientation. • Visualize which ways the object can be rotated within the given number of moves. • Provide possible solutions and check in with Partner 1 at each rotation. Shadow The same as in the rotation challenge except the players need to challenge understand how to translate 3D objects into 2D representation. E.g., if three cubes are stacked horizontally one after another, they will be projected as one square on the 2D-surface. Players also need to communicate the position of the shadow squares on a 3x3 grid. Color The same as in the rotation challenge except Partner 2 sees the position orientation of each color but not the object itself. This puzzle invites more challenge analytical thinking; instead of visualizing each color separately, their position can be deducted based on colors that appear on the opposite sides. Continued

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Table 6 Continued Jump field Partner 1: challenge • Communicate the position and sequence of tiles which should be stepped on. • Constantly check in with Partner 2 about the direction they are facing to make sure the views are aligned. • Provide help in deciding how to get from one tile to another. • In the timed version (the tile map randomly rotates 90, -90 or 180 degrees every 15 seconds), continuously visualize the orientation of Partner 2 on their field.

Partner 2: Continuously communicate their orientation on the field by using landmarks (e.g., the green arrow or walls) or developing a special system of coordinates (e. g., labeling each tile with a number and a letter or explaining their position in the structure of columns and rows). Maze Partner 1: Similar to the Jump field challenge except Partner 1 also challenge needs to constantly keep in mind the current location of Partner 2 and make sure the directions are given based on Partner’s 2 frame of reference.

Partner 2: • Clearly communicate what landmarks they see around and which way they are facing. • Memorize which routes were already taken and remember the set of previously given instructions. Stepping Both partners: cube • Visualize the position of red shapes on the cube side when the challenge cube moves so that that side faces down. • In the inverted version, visualize inverted cube movement. • In the continuous step version, visualize the same thing for every step of the cube without being able to check the result step by step. Trust Both partners: challenge • Establish and continuously communicate a common frame of reference and prominent landmarks. • Specify the angle of invisible paths (45 or 90 degrees).

The necessity of collaboration is further established by the game progression mechanic. To clear the level, both players need to step through a portal that leads them to the next level. For the active problem-solver, the portal appears only when the challenge

104 is solved. Along with the portal, a 7-symbol code sequence is revealed. This sequence should be communicated to the partner; inputting the sequence reveals that partner’s portal to the next level (Figure 22). This mechanic ensures that both players cannot advance to the next level without each other’s help, and even if they do, clearing the next level without their partner is very hard if not impossible. The code sequence consists of 5 unique symbols that are arranged in a specific order to form a 7-symbol sequence, and any symbol can be repeated multiple times or not used at all. Guessing the code by chance is very unlikely.

Figure 22. Code Pad (on the Left) and a Portal (on the Right). The challenges were designed to allow players a variety of solutions. For example, in the rotation challenge, players can rotate the object in any direction as long as they get the specified result within the established number of moves. In the jump field challenge, players can jump from platform to platform, walk around the field to get to the next platform in sequence or walk backward stepping only on the platforms they already solved correctly to make their way to the next platform. While the result is always

105 predefined, the means of getting to the result may vary and are specific to each team of players.

The game features 5 branches (they are called “Levels” in the game; Figure 23).

The first branch is a tutorial: it features a room in which players can practice controls and

4 levels which introduce players to the types of puzzles to follow. Branches 1-3 feature 5 challenges of similar type with a few twists and variations (the mechanics of solving them stay the same). Branch 4 is one big space with 4 different challenges, the last of which concludes the game. Each subsequence branch is more difficult than the previous ones to help students gradually build self-efficacy. The level of challenge was assessed and adjusted based on multiple playtesting sessions of the game. For research purposes, each branch could be accessed separately from the main menu - to make sure that students can access all levels even if they have not finished the previous ones. I also implemented a secret code that can teleport the player to any level in case of technical problems.

Figure 23. Main Menu.

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In the Tutorial Branch and Branch 1, players are given prompts with explanations and tips about the challenges and communication with their partners. In subsequent branches, the number of such prompts decreases to almost non-existent except for situations when new information is presented. Although the prompts help players navigate game challenges, a lot of information is not stated explicitly, allowing them the freedom to explore and make their own conclusions. For example, in the field jump challenge players are never told that they can step off the field and walk to the next platform on the outside but nothing stops them from figuring it out on their own.

A few other important game design choices include game goals and reward systems. Games tend to offer players predefined goals which vary widely; they can include learning what happened in the story, collecting all rare items, getting public recognition from other players, and many more. Some games do not impose predefined goals on players; a famous example is Minecraft, an open-world sandbox game in which players can decide what they want to do for themselves. In Bond, the goal is very simple: to get out of the space in which the player is trapped. In a way, it is a puzzle escape game.

However, the game does not have any typical reward system elements that would set other (implicit) goals for players. Solving puzzles does not give players any points or items; there are no scores or leaderboards, and there is no inventory with items. The only reward is the satisfaction that comes with cracking a puzzle and the curiosity about what is next.

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Technology

The game was made using Unity 3D game engine (version 2019.1.1f1) and designed to be played using two types of technology: a desktop computer and a mobile Virtual

Reality headset with a smartphone (Figure 24).

Figure 24. Components of the Required Hardware for the Game. The game on the desktop computer uses standard computer accessories for game controls (a mouse and a keyboard). The mobile Virtual Reality technology consists of two parts. One is a smartphone that runs the game. Any smartphone compatible with

Virtual Reality should be able to run a VR game although certain restrictions exist depending on the type of the headset and the phone operating system. The second element is a mobile VR headset. Such headsets range in price (typically from $5 to $100 and up) and build (some famous examples are Google Cardboard, Samsung Gear VR,

Google Daydream) but they are generally very affordable compared to more advanced headsets connected to desktop computers (such as HTC Vive or Oculus Rift). Mobile VR

108 apps use a split-screen that together with the headset lenses create a stereoscopic image and a sense of immersion.

The main challenge in addressing the gameplay in Virtual Reality was deciding the type of movement and camera controls available to the player. This problem was solved by using code that reads players’ movements in the physical world and uses it as an input for movement in the virtual space. In other words, players can freely walk around and jump in Virtual Reality while walking/jumping in place in the real world.

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Chapter 4. Method

Research design

The present study was conducted using a quasi-experimental design. The participants were assigned to one of the two conditions: experimental (2-week game-based intervention) and control (business-as-usual, no intervention). The assignment of classrooms to a particular condition depended largely on logistical reasons, including the following:

1. The intervention could not have been administered at several classes

simultaneously due to a limited amount of equipment. The team had only 7 video

cameras, 16 smartphones and 16 headsets available.

2. The class schedule was different for some sections but overlapped or coincided

for others. Each section was taught as one 50-minute class a day, 5 times a week.

3. Equipment setup took about 40 minutes every time, making it more viable to set it

up once for two classes in a row as opposed to setting it up multiple times

throughout the day.

4. Because each teacher had sections participating in both conditions, we tried to

pick classes that were set apart in the class schedule so that students in the control

condition did not interact with the experimental procedure.

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Instruments and measures

Three types of data were collected for this study.

Demographic and STEM performance data, including gender, age, grade level, ethnicity, mean STEM course grade pre- and post-intervention. This data was pulled from school educational records with the permission of the school district.

Visuospatial performance was measured by the Spatial Reasoning Instrument

(https://www.silc.northwestern.edu/spatial-reasoning-instrument/) for middle school students (SRI; Ramful, Lowrie, & Logan, 2017). The questionnaire consists of 30 questions, each with 4 options and only one correct answer. The scale includes three subscales (10 questions each): Mental Rotation, Spatial Orientation, Spatial

Visualization. The paper version of the test was transferred to an online platform. Sample questions (original questions include images): “Below is a picture of two dogs. Which one of the following shows the picture after a 90 degree turn to the right?”; “Briana placed a hamster at the start of a maze as shown below. The hamster ran through the maze. It turned to its right, then turned left, then turned right. Where did the hamster finish?”. The scale was validated by the authors, and the instrument showed good reliability in the current study as measured by the Cronbach’s alpha (Cronbach’s α = .83 at Time 1 and Cronbach’s α = .86 at Time 2).

Visuospatial self-efficacy (VSSE) scale was used to measure participants’ visuospatial self-efficacy. The scale (available at https://archive.org/details/vssemedia) consists of 25 items and 5 subscales, including (1) Rotation and symmetry; (2) Isometric and orthographic views; (3) Folding Flat Patterns; (4) Real-Life Tasks: Implicit VS Skill

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Use; (5) Real-Life Task: Explicit VS Skill Use. Sample items include (without images):

“I can solve three dimensional puzzles like Rubik’s cube”; “I can read a map without turning it to align it with the direction I am going”; “I can visualize rotating any abstract

3D object around any one axis and what the result looks like”; “For any abstract 3D object, I can visualize all possible ways to cut it with a place into two symmetrical halves

(if such a plane of symmetry exists)”. The scale validity was established by our research team (the manuscript is currently under review), and the instrument showed robust reliability in the current study (Cronbach’s α = .93 at Time 1 and Cronbach’s α = .95 at

Time 2).

Procedure

The setup and participants

The study took place in three middle schools in a suburban district of a mid-western city in the United States. The district serves more than 16,000 students in grades K-12, predominantly White (about 75%) and about 25% economically disadvantaged. Our research team contacted one of the school district coordinators to explain the study details and secure permission to conduct the study. We were introduced to three teachers, one from each school, who were interested in participating. All of them taught the same elective, introductory-level class comprising elements of 3D modeling, robotics and coding, and some engineering concepts. Teacher 1 (School 1) and Teacher 2 (School 2) each taught 3 sections of the class, and Teacher 3 (School 3) taught 5 sections of the

112 class, totaling to 11 class sections across the three schools. All teachers followed the same curriculum with small modifications.

Our team observed several classes in all three schools in early May of 2019 and learned about the course content, including the course topics and main projects. The following summer was spent developing the intervention game and communicating with teachers to obtain their feedback on game progress. The game was initially play tested by the members of the research team, then presented and play tested at a large Mid-Western gaming convention, and was substantially modified following the playtesters’ feedback.

We started the student recruitment process in October of 2019. The 11 class sections were divided into control (5 sections) and experimental (6 sections) groups

(Table 7). Teacher 1 and Teacher 2 had 1 control and 2 experimental sections respectively; Teacher 3 had 3 control and 2 experimental sections.

Table 7. Class Sections by Condition and Teacher.

Teacher 1 Teacher 2 Teacher 3 Control 1 section 1 section 3 sections Experimental 2 sections 2 sections 2 sections

Student recruitment

During student recruitment, several research team members visited each classroom during class time and briefly described the study to the students. The students then completed an assent form if they chose to participate in the study. Each student was given a parental permission form sealed in an envelope, and students in the experimental condition also received a cybersickness form to be completed by their guardians. The 113 cybersickness form was used to screen for serious conditions that could be triggered by engaging with VR technology. The students had two weeks to bring the signed (or not signed, depending on the guardian’s decision) forms back to their teacher. Student data for this study were analyzed only when two conditions were satisfied: (1) the student assented to participation; (2) the guardian gave permission for their child to participate in this research. Out of 282 students, 169 gave their consent and received parents’ permission to participate in the study.

Assessments

Time 1 assessments were administered shortly after the documentation documenting consent to participate was collected on the week of October 28. The students in all 11 sections were asked to take two surveys using a secure online platform, the visuospatial self-efficacy survey, and the Spatial Reasoning Instrument survey. They were given a full class period to complete the surveys but were allowed to go back to classwork once they were finished. The Time 1 assessments were administered during one week and were the same across all 11 sections. Time 2 assessments (the same surveys) were administered in the same way the next week after the end of the intervention. The timeline of the data collection and the intervention is shown in Table 8.

Table 8. Experiment Timeline.

Pre-assessment Time 1 Intervention Intervention Time 2 assessment Week 1 Week 2 assessment Student The week of The week of The week of The week of recruitment (2 October 28 November 4 November 11 November 18 weeks)

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Intervention

General description

The intervention took place on the weeks of November 4 and November 11. Depending on the circumstances, the intervention was administered in 4 class sections (of two different teachers) on the same day or in 2 class sections (of one teacher). Each experimental section had two intervention sessions per week and 4 intervention sessions in total.

The groups in the control condition were business-as-usual and engaged in typical class activities. The only research-related activities they did were surveys. The experimental groups engaged with the game developed for the intervention. All equipment was set up in advance before the class started. Students who opted out of the study still played the game; in other words, the game was integrated as part of the curriculum. Those students were paired together and not video recorded.

Pilot session

The first session of the intervention served as a pilot to test technology performance and the logistics of the intervention procedures. Originally, the game had three versions built- in (accessible from the game’s main menu) in which the challenges were the same but shuffled in a different order. This was done to account for the proximity of each pair playing the game so that the students do not get distracted by other people working on the same challenge. Players did not have access to separate levels. Instead, the game was one continuous level. The game also featured a saving and loading system so that students

115 could pick up where they left off and could differentiate their save slot from the save slot of other students working on the same computer. This setup did not work well due to a few problems:

• Students were confused about which game version they should choose and in some

pairs students ended up choosing different game versions.

• Some students were confused about the saving and loading procedures.

• Students played at a different pace and were very engaged so they did not seem to be

distracted by other students. This made different versions of the game an unnecessary

element.

• Students seemed to progress through the game more slowly than we expected, and the

absence of differentiated levels was inconvenient.

Relying on these observations, we decided to simplify the game architecture in the following ways:

• We got rid of different game versions, leaving only one game version by default.

• We separated all challenges into 5 branches: Practice, Level 1, Level 2, Level 3,

Level 4. Each branch was supposed to be finished during 1 intervention session, and

each branch features 4-5 challenges.

• We removed the saving/loading functionality. At each session, participants started a

new level. If they needed to move to a different level, they could do it freely in the

main menu. If students needed to move to a different challenge within the same level

or on a different level, the research team administered code known only to the team

members that teleported students to the desired challenge. This was mostly done

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when technology malfunctioned or when one partner moved on to the next level

without telling the required code to the other partner (but when the challenge was

already solved).

Initially students were paired based on their visuospatial scores, and mixed-ability pairs were formed (one student with a lower score on the SRI instrument, one student with a higher score on the SRI instrument - based on Time 1 assessment results).

However, this setup proved to be very complex to work with in real classroom settings. It was hard to assign new partners on the fly when some students were not present in class, and many students were upset because they did not get a chance to work with partners they would choose for themselves - regardless of their visuospatial performance scores.

We realized that letting students choose their partners had a positive impact on their communication and simplified intervention procedures. It was also closer to typical classroom settings in which one teacher has to manage multiple students at the same time.

Sessions 2-4

The other three sessions were based on the following structure:

• The class was divided into two halves. The first half played for 25 minutes, and

the second half for the remaining 25 minutes. When not playing, the students

went back to their regular class activities. Class size ranged from 20 to 32

students, meaning 5 to 8 pairs of students were playing the game at any given

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time. This helped us dedicate a separate camera to record each pair’s interactions

and troubleshoot technical problems more efficiently.

• The first half of the students were invited to a separate space in the classroom

equipped with desktop computers (available in the classrooms of Teacher 1 and 3)

or stayed in the same classroom space but were seated to allow the other half to

work on other tasks (Teacher 2) (Figure 25).

Figure 25. Classroom Setup for the Intervention.

• The students were allowed to pair up with anyone in their sub-group. The students

who could not be video recorded could pick any partner in their sub-group as long

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as they were not allowed to be recorded as well. Some students worked in groups

of 3 if their subgroup had an odd number of participants.

• Each pair had two sets of equipment: a desktop computer with a mouse and a

keyboard and a smartphone with a mobile VR headset. Each pair decided who

started with what device. There were no strict rules about switching devices but

we did ask them not to switch devices in the middle of solving a challenge if

possible, although most of the students still did so. If a student experienced side

effects of using VR (dizziness, sickness), they were encouraged to switch with

their partner and use the computer or to use the phone without the headset.

• The students were directed to go to a specific level each time:

o Session 2: Level 1

o Session 3: Level 2

o Session 4: Level 4

▪ We decided to skip level 3 based on students’ pacing in hopes to

provide some closure when students finish the game (Level 4 is the

last level).

• The students engaged with the game for about 25 minutes. The research team was

there at all times to record students’ interactions and to provide technical support.

The teachers sometimes provided support to the playing students and sometimes

worked with the other half of the class.

• After 25 minutes, the first sub-group was asked to stop and switch places with the

second sub-groups. The entire procedure was repeated for that sub-group as well.

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The only change we made to this structure was during session 2. We noticed that some students were confused about some of the game challenges. We spent the first 5 minutes of the class showing the screenshot of each challenge type and very briefly explaining the essence of the challenge. We also modeled the problem-solving process for a couple of challenges and emphasized the communication patterns important to clearing the game. This only happened during the second session.

As students engaged with the game, there were at least 2 or 3 research team members in the room at any given time helping with technology-related questions and assisting students when they got stuck on certain challenges.

The students in the control condition continued with their standard curriculum

(which was very similar across all classrooms) and did not engage with the game. We implemented the intervention so that the classes in the control condition did not come in direct contact with the experimental classes (i.e., the control classrooms did not have classes right before or after the experimental classes). The teachers did not mention the game to the students in the control condition. However, we could not control for the contamination effect that could happen when students from control and experimental conditions communicated in other classes or in their free time. According to the teachers, the students in the control classrooms did not ask about the intervention and did not talk about it with each other but we do not know the extent to which the information about the intervention was shared between the two conditions.

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Analysis

The variables used for the analysis and their level of measurement and values are presented in Table 9.

Table 9. Study Variables.

Variable Values Role in the analysis Condition 0 = control condition Independent variable 1 = experimental condition Ethnicity 0 = White Covariate 1 = Other Gender 0 = male Covariate 1 = female Age Continuous scale, in full years Covariate Videogame 0 = no to little experience Covariate (the original 4 experience 1 = some to a lot of experience categories (no, little, some, a lot of experience) were collapsed into a binary variable for the purposes of the regression analysis) VS performance The number of correct answers Covariate at Time 1; Dependent T1 and VS (0 to 30) variable at Time 2 performance T2 VSSE T1 and T2 The mean of the responses (25 Covariate at Time 1; Dependent questions) on the Likert scale (1 variable at Time 2 = Not confident at all; 7 = Very confident) Pre-intervention The average of final raw marks Covariate STEM grade of all STEM courses at the end of the semester preceding the intervention (from May 2019) Post- The average of final raw marks Dependent variable intervention of all STEM courses at the end STEM grade of the semester during which the intervention was held (from December 2019-January 2020)

The statistical analysis was motivated by the theoretical model derived from the literature on visuospatial thinking (Figure 26). According to previous research, individual

121 and developmental differences (including gender, ethnicity, age, and socioeconomic status) can play a role in differential performance on visuospatial assessments (Lauer,

Yhang, & Lourenco, 2019; Levine, Vasilyeva, Lourenco, Newcombe, & Huttenlocher,

2005; Linn & Petersen, 1985; Sanders, Wilson, & Vandenberg, 1982). Videogame experience has also been linked to VS skills (Spence & Feng, 2010; Uttal et al., 2013).

Likewise, visuospatial skills are linked to STEM achievement (Uttal et al., 2013), and self-efficacy is linked to performance and course choices (Honicke & Broadbent, 2016;

Uitto, 2014; Williams & Williams, 2010).

All of these variables were included in the study models. In particular, ethnicity, gender, age, videogame experience are included as covariates to controls for demographic factors. VS performance and VSSE at Time 1 are included as covariates to make sure that the effects are not due to students’ original performance on these assessments at Time 1. Pre-intervention STEM grade is included to control for the effect of past performance on the Time 2 performance. The focal variable of interest is condition (i.e., the impact of the intervention).

These covariates and the focal independent variable are used to predict three dependent variables in three separate analyses: VS performance at Time 2, VSSE at Time

2, and post-intervention STEM grade (Figure 26).

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Figure 26. The Study Model. Multiple Regression analysis was used to answer the research questions of the present study. It was chosen over another alternative, Hierarchical Regression Modeling, due to the potential problems associated with a small number of level-2 clusters (i. e., classrooms; McNeish & Stapleton, 2016).

Before performing multiple regression analysis, the data were screened to make sure all assumptions required for the analysis were met (Cohen, Cohen, West, & Aiken,

2013). The screening indicated that the data were suitable for the chosen analysis for all three outcomes variables. The assumption of independence of observations was met

(Durbin-Watson values were within the 1-3 range); no outliers were identified; multicollinearity was not a concern (VIF and tolerance values were within an acceptable range); the standardized residual histogram and the normal P-P plot of standardized 123 residuals showed an approximately normal distribution of errors; based on the scatterplot of standardized residuals, the assumption of homoscedasticity and linearity was met.

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Chapter 5. Results

Descriptive statistics

A total of 169 students participated in the study (out of 282 students enrolled in all 11 classes; the consent rate was about 59%). Participants’ age ranged from 12 to 14 years old (M=12.48; SD = .63). Most of them were in 7th grade (81%) and the rest were 8th- graders. Consistent with the observed gender gap in STEM course enrollment (Bergeron

& Gordon, 2017), 71.6% of the participants were male and 28.4 were female. The sample was predominantly composed of white students (74%); 7.1% were identified as Multi-

Racial, 8.3% as Asian, 4.7% as Black/African American and 4.7% as Hispanic, and 1.2% as American Indian/Alaskan Native. The majority of students reported a lot of gaming experience (60.5%), 27.2% - some gaming experience, and 12.3% - no gaming experience.

The demographics in the control (n= 73) and experimental (n = 96) conditions were comparable and reflected the characteristics of the general sample as indicated by the Chi-Square tests (videogame experience: χ2 = 1.29, p = .26; ethnicity: χ2 = 1.13, p

= .29; gender: χ2 = .19, p = .66) and the independent t-test (age: t = .80, df = 166, p = .42).

The breakdown of the demographics by condition is shown in Table 10 and Figures 27-

30.

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Figure 27. Participants' ethnicity.

Figure 28. Participants' grade level.

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Figure 29. Participants' gender.

Figure 30. Participants' videogame experience.

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Table 10. Sample Demographics.

Control Experimental Sample (n = 73) (n = 96) (n = 169) Age Mean (SD) 12.52 (.65) 12.44 (.61) 12.48 (.63) Median 12 12 12 Min - Max 12 - 14 12 - 14 12 - 14 Gender Male 51 (69.9%) 70 (72.9%) 121 (71.6%) Female 22 (30.1%) 26 (27.1%) 48 (28.4%) Grade 7th 59 (80.8%) 79 (82.3%) 138 (81.7%) 8th 14 (19.2%) 17 (17.7%) 31 (18.3%) Ethnicity American Indian/Alaskan Native 0 (0%) 2 (2.1%) 2 (1.2%) Asian 6 (8.2%) 8 (8.3%) 14 (8.3%) Black/African American 2 (2.7%) 6 (6.3%) 8 (4.7%) Hispanic 4 (5.5%) 4 (4.2%) 8 (4.7%) Multi-Racial 4 (5.5%) 8 (8.3%) 12 (7.1%) White 57 (78.1%) 68 (70.8%) 125 (74.0%) Videogame experience A lot 43 (61.4%) 55 (59.8%) 98 (60.5%) Some 16 (22.9%) 28 (30.4%) 44 (27.2%) Little 0 (0%) 0 (0%) 0 (0%) None 11 (15.7%) 9 (9.8%) 20 (12.3%)

The missing data analysis performed in SPSS (ver. 26) indicated that missing data on the variables ranged from 0 to 6%. The analysis of the missing data patterns revealed that most of it was due to students’ absence at the time of assessment (usually either at

Time 1 or at Time 2). No other patterns were indicated by the analysis. The case-wise deletion method was used to handle missing data.

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RQ1: Did the game-based intervention improve students’ VS self-efficacy?

Multiple linear regression was conducted to predict post-intervention VS self- efficacy based on the demographic factors (ethnicity, gender, age) and several covariates

(including videogame experience, pre-intervention VS performance, pre-intervention

VSSE, and pre-intervention STEM grades) and the treatment (intervention vs. no intervention). The variables were entered in three blocks. Descriptive statistics of the variables for this analysis and the analysis for the other two research questions are presented in Table 11.

Table 11. Multiple Linear Regression Results: Descriptive Statistics.

Variable Overall mean Control condition Experimental condition (SD) mean (SD) mean (SD) VS performance T1 18.40 (5.44) 19.13 (5.63) 17.83 (5.25) VS performance T2 18.99 (6.06) 19.65 (6.05) 18.50 (6.06) VSSE T1 4.13 (.97) 4.13 (1.04) 4.13 (1.04) VSSE T2 4.44 (1.06) 4.27 (1.10) 4.57 (1.02) STEM grade (pre) 89.82 (8.24) 89.56 (8.43) 90.04 (8.12) STEM grade (post) 92.59 (6.62) 92.97 (6.15) 92.29 (6.98)

The first block consisted of demographic information and videogame experience.

The model yielded an R2 of .017 (adjusted R2 of -.01), indicating that these covariates alone do not have explanatory power. With performance covariates, the model explained

69% of the variance, and adding condition to the equation increased the explanatory power of the model to 71%. ANOVA results indicated that the final model was significant (F(8, 139) = 46.18, p < .001).

Based on the analysis results, the regression equation with standardized coefficients looks as follows (full results are presented in Table 12):

129

VSSE T2 = .46 + .03(ethnicity) -.06(gender) -.01(age) -.10(videogame exp)

+.15(VS perf T1) +.78(VSSE T1) +.03(STEM grade (pre)) +.16(condition)

Assuming the other variables are held constant:

• Students with some or a lot of gaming experience the post-intervention scored

-.10 deviations lower on post-intervention VSSE than those with little to none

video game experience.

• With every increase of one standard deviation in pre-intervention VS

performance, the post-intervention VSSE went up by .15 standard deviations;

• With every increase of one standard deviation in pre-intervention VSSE, the post-

intervention VSSE went up by .78 standard deviations;

• Students in the experimental conditions (who participated in the intervention)

scored .16 standard deviations higher on the post-intervention VSSE than those in

the control condition (business-as-usual).

Table 12. Results of Multiple Regression Analysis (VSSE T2 as Dependent Variable)

Variables β (SE) β (stand.) t p Intercept .46 (1.15) .40 .68 Ethnicity .08 (.11) .03 .70 .48 Gender -.14 (.11) -.06 -1.20 .23 Age -.02 (.08) -.01 -.14 .75 Videogame experience -.33 (.15) -.10 -2.15 .03 VS performance T1 .03 (.01) .15 2.80 .006 VSSE T1 .85 (.05) .78 16.41 .001 STEM grade (pre) .004 (.01) .03 .58 .56 Condition .35 (.09) .16 3.56 .001

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RQ2: Did the game-based intervention improve students’ VS performance?

Multiple linear regression was conducted to predict post-intervention VS performance based on demographic factors (ethnicity, gender, age) and several covariates (including videogame experience, pre-intervention VS performance, pre-intervention VSSE, and pre-intervention STEM grades) and the treatment (intervention vs. no intervention). The variables were entered in three blocks.

The first block consisted of demographic information and videogame experience.

The model explained only 2.4% of the variance as measured by the adjusted R2. With performance covariates, the model explained 72.3% of the variance. Adding condition to the equation did not make any substantial changes to the model's explanatory power.

ANOVA results indicated that the final model was significant (F(8, 139) = 48.56, p

< .001).

Based on the analysis results, the regression equation with standardized coefficients looks as follows (full results are presented in Table 13):

VS perf T2 = 6.97 -.054(ethnicity) - .09(gender) -.09(age) -.05(videogame exp)

+ .78(VS perf T1) -.01(VSSE T1) + .13(STEM grade (pre)) -.01(condition).

Assuming the other variables are held constant:

• With every increase of one standard deviation in age, the post-intervention VS

performance goes down -.09 standard deviations;

• With every increase of one standard deviation in pre-intervention VS

performance, the post-intervention VS performance goes up by .78 standard

deviations;

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• With every increase of one standard deviation in pre-intervention STEM grade,

the post-intervention VS performance goes up by .13 standard deviations.

Table 13. Results of Multiple Regression Analysis (VS performance T2 as Dependent Variable).

Variables β (SE) β (stand.) t p Intercept 6.97 (6.43) 1.08 .28 Ethnicity -.74 (.60) -.054 -1.23 .22 Gender -1.17 (.63) -.09 -1.86 .065 Age -.89 (.43) -.09 -2.08 .039 Videogame experience -.82 (.86) -.05 -.95 .34 VS performance T1 .87 (.06) .78 15.41 .001 VSSE T1 -.07 (.29) -.01 -.25 .80 STEM grade (pre) .09 (.04) .13 2.62 .01 Condition -.07 (.54) -.01 -.12 .90

RQ3: Did the game-based intervention improve students’ STEM performance?

Multiple linear regression was conducted to predict post-intervention STEM course grade based on the demographic factors (ethnicity, gender, age) and several covariates

(including videogame experience, pre-intervention VS performance, pre-intervention

VSSE, and pre-intervention STEM grades) and the treatment (intervention vs. no intervention). The variables were entered in three blocks.

The first block consisted of demographic information and videogame experience.

The model yielded an adjusted R2 of .014, indicating that these covariates only explain

1.4% of the variance. With performance covariates, the model explained 68.5% of the variance, and adding condition to the equation increased the explanatory power of the model to 68.7%, although the change in R2 was not significant (F change = 1.83, df1 = 1, df2 = 139, p = .18). ANOVA results indicated that the final model was significant (F(8,

139) = 41.35, p < .001). 132

Based on the analysis results, the regression equation with standardized coefficients looks as follows (full results are presented in Table 14):

STEM grade (post) = 31.52 + .10(ethnicity) -.01(gender) + .02(age) -.05(video

game exp) +.11(VS perf T1) -.04(VSSE T1) +.79(STEM grade (pre)) -.06(condition)

Assuming the other variables are held constant:

• Non-white students’ STEM grade (post) was .10 standard deviations higher than

that of white students.

• With each increase in VS performance at Time 1 by 1 standard deviation,

students’ post-intervention STEM grade went up by .11 standard deviations.

• With each increase in pre-intervention STEM grade by one standard deviation,

students' post-intervention STEM grade went up by .79 standard deviation.

Table 14. Results of Multiple Regression Analysis (STEM grade (post) as Dependent Variable).

Variables β (SE) β (stand.) t p Intercept 31.52 (7.44) 4.23 .001 Ethnicity 1.48 (.70) .10 2.12 .036 Gender -.10 (.73) -.01 -.13 .89 Age .24 (.49) .02 .49 .62 Videogame experience -.97 (.99) -.05 -.97 .33 VS performance T1 .13 (.07) .11 2.03 .04 VSSE T1 -.25 (,34) -.04 -.73 .47 STEM grade (pre) .64 (.04) .79 15.16 .001 Condition -.85 (.63) -.06 -1.35 .18

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Chapter 6. Discussion

Visuospatial skills are one of the cornerstones of success in the STEM field; they predict STEM achievement, the choice of STEM courses, majors and career paths, and retention (Wai, Lubinski, & Benbow, 2009; Cheng & Mix, 2014). The disparity in VS skills also contributes to the gender gap that has plagued the STEM domain for many years (Ganley, Vasilyeva, & Dulaney, 2014). These skills are malleable and can be improved through training (Uttal et al., 2013). However, as of 2020, few comprehensive training programs are available, and little is known about the mechanisms implicated in the improvement of these skills. The current study proposes a solution rooted at the intersection of three disciplines - educational psychology, educational philosophy, and game design - to bridge this gap.

The proposed game-based intervention leveraged the principles of self-efficacy

(Bandura, 1977; 1982), educative experiences and democratic education (Dewey, 1916), and bridged them with the principles of game design already employed by successful commercial games. The developed game Bond was administered in 11 middle-school classrooms to assess its impact on students’ VS self-efficacy, performance, and STEM performance. The general results indicated that the game indeed was successful in improving students’ VS self-efficacy, but these gains have not yet significantly translated into their performance outcomes even though a positive trend was revealed. Overall, this

134 study contributed to our understanding of the development of visuospatial skills and self- efficacy, serious game design, and suggested a new way of approaching visuospatial interventions.

Design Question: The principles of serious game design

The first question driving this study was focused on the processes and principles that should guide the development of a serious game based on pedagogical foundations and the principles of game design. The intervention game Bond was designed following the identified principles of democratic education proposed by John Dewey and the principles suggested by the self-efficacy theory. The game is available for download at learnspatially.com. I recommend the following design principles to be implemented when designing serious games for education:

1. Create continuous experiences that build on previous experiences. Pick a general

theme or develop a plot progression that will put players’ actions in meaningful

and compelling contexts.

2. Create game challenges that organically fit in the context of the game, the plot,

and the story progression. For example, making players solve a math challenge to

use a magic spell might not be the most organic way to create meaningful learning

opportunities.

3. Provide players an opportunity to adjust the level of challenge they are facing or

craft the progression of challenges so that their difficulty increases incrementally

in line with players’ skill development.

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4. Allow the players the autonomy to set their own goals and choose their own

means of problem-solving. The game can have predetermined goals but providing

players with autonomy is essential for creative problem-solving and letting

players choose their own aims.

5. Focus on intrinsic motivators (enjoyment of the play and a sense of progress, of

the ‘a-ha’ moments, of the discovery of the lore and the story). Do not use games

for assessment purposes. It is possible to incorporate extrinsic metrics of progress

(e. g., experience points), but doing so should support players’ reflection on their

learning progress as opposed to serving as a strictly defined measure that they

must achieve.

6. Provide opportunities for collaborative problem-solving. This is one of the central

postulates of Deweyan philosophy. Games can be used to promote players’

learning about how to engage in collaborative problem-solving (as a meta-skill)

and have significant potential to implement multi-player solutions. Collaborative

play can be structured in many ways. The game can be designed as a local co-op

in which players have to be in the same space to play. It can be a co-op that does

not require the presence in the same space; communication can be outsourced to

an external medium (e. g., talking via a voice chat). It can be an online

multiplayer game that has a built-in mechanism for communication (e.g., built-in

voice and text chat). Collaboration does not have to be based on verbal

communication. Games can encourage players to collaborate through actions only

(e. g., moving objects around together) or through symbols (which is the case in

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Journey, where players use ‘pings’ to communicate). Game challenges can be

designed in a way that requires collaboration (e.g., each player has incomplete

information but together they can figure out the answer) or encourages it (e.g., the

quest posting system in Monster Hunter).

7. Design game challenges to encourage experimental thinking and the use of the

method of science. Let players test hypotheses by making the game world highly

interactive and supporting their mistakes. For example, do not punish players by

making them lose something if they fail. It is possible to restrict players from

performing certain actions that ‘cheat’ the game but such restrictions should not

feel like a punishment or should be justified within the context of the game story

so that the players consider it fair. In some situations, giving players opportunities

to cheat the game might actually be desirable if it prompts them to engage in

creative problem-solving. As an example, there is a dedicated community of game

speedrunners that try to finish games as fast as possible (within minutes or hours).

Some of them do what is known as tool-assisted speedrunning, which is designed

by writing an algorithm controlling the game input sequence in the most optimal

way. The complexity of calculations and creative problem-solving in such

speedruns is enormous. Super Mario 64 tool-assisted speedrun is a clear

illustration of this. The principles implemented to achieve a world speedrun

record of 4 minutes and 20 seconds include (but are not limited to) route

optimization, parallel universes, complex analysis of the game world physics,

decomposition of in-game actions frame by frame, and their interaction with game

137

control input (see an example of a technical explanation of these principles here).

In other words, if cheating the game requires more creative effort than winning it

through regular play, perhaps, it can justify leaving opportunities for players to do

so.

8. Provide opportunities for players to form game communities. It can be a forum

dedicated to the game, or simply providing space on a school platform for a

community of students playing the game to share their experiences. In well-

designed games with ill-defined problems that require collaborative problem-

solving, such communities can be formed naturally by players themselves.

Research Question 1: Visuospatial self-efficacy

The first research question was concerned with the effect of the game-based intervention on students’ VS self-efficacy. The alternative hypothesis was that there will be an observed positive effect of the intervention on this outcome. The results of Multiple

Regression Analysis supported this hypothesis. Students who participated in the game- based intervention scored significantly higher on the post-intervention VSSE assessment, although the effect size was relatively small. Considering the short period of time the intervention took, this result is encouraging; it is possible that a longer intervention could produce more substantial results. This finding demonstrates that the developed game can be an effective instrument in improving students’ VS self-efficacy.

Three other variables were associated with VSSE. Students with a higher baseline

VSSE score and a higher pre-intervention VS performance demonstrated higher post-

138 intervention VSSE. This is in line with the self-efficacy theory (Bandura, 1977; 1982).

Students with higher initial levels of self-efficacy had a head start compared to those with lower VSSE. Because mastery experiences are one of the strongest sources of self- efficacy beliefs, students who scored higher on pre-intervention VS performance assessment might have had more positive mastery experiences with visuospatial tasks, feeding into higher self-efficacy levels.

Surprisingly, students with some or a lot of gaming experience scored -.10 deviations lower on post-intervention VSSE than those with little to none video game experience. Even though the effect size is very small, gaming experience was a significant negative predictor of VSSE. While no studies considered the relationship of gaming and VSSE, one would expect to find a positive relationship based on the literature that links gaming experience with better visuospatial skills (Cherney, 2008;

Cherney, Bersted, & Smetter, 2014; Shute, Ventura, & Ke, 2015; Spence & Feng, 2010).

Future studies could explore potential mediating or moderating variables that explain this negative relationship.

Research Question 2: Visuospatial performance

The second research question of interest was about the effect of the game-based intervention on students’ VS performance. The alternative hypothesis was that there will be an observed positive effect of the intervention on this outcome. The results of Multiple

Regression Analysis, however, did not support this hypothesis. Playing the game did not

139 improve students’ performance on the visuospatial test. Two possible explanations can be proposed to account for this result.

First, it is possible that there was no transfer effect between the performance on the in-game tasks and the performance on the spatial assessment due to their different nature. While other studies demonstrated some transfer effects of VS training in the past

(Uttal et al., 2013), the mechanisms of transfer and its extent are still not clear. More research is needed to determine effective ways of achieving the transfer of visuospatial skills.

Second, because the intervention did increase students’ visuospatial self-efficacy and the visuospatial performance post-test was administered right after the intervention, it is possible that there was not enough time for the increase in self-efficacy to manifest positive changes in performance. Self-efficacy impacts the choice of activities, persistence, and motivation (Bandura, 1986), but these factors need to be translated into practice for performance to improve.

Although the intervention did not have an impact on students’ VS performance, their age, pre-intervention VS performance and pre-intervention STEM grade were significant predictors. Research has shown that previous performance is a strong predictor of future performance (Schneider & Preckel, 2017). Because the VS performance test was the same at both times of assessment, students who did better on it the first time were likely to have an advantage at the second time of administration.

Students with higher pre-intervention grades could have better visuospatial skills that led to better grades in the first place, thus impacting their post-intervention VS performance.

140

Interestingly, age had a negative relationship with VS performance. The teachers who participated in the intervention and a school administrator noticed that students in the 8th grade tend to be assessed using more strict criteria and engage in more challenging courses, which increases the chance of getting lower grades compared to their previous course grades and the 7th-graders in the sample. Additional descriptive analysis of STEM grades for 7th and 8th grades in the sample showed that students in the 8th grade indeed had lower STEM grades both pre- and post-intervention. Perhaps, this could have had a negative impact on students’ VS self-efficacy, which, in turn, determined lower engagement level during the VS test.

Research Question 3: STEM performance

The fourth research question was concerned with the effect of the game-based intervention on students’ post-intervention STEM grades. The alternative hypothesis was that there will be an observed positive effect of the intervention on this outcome. The results of Multiple Regression Analysis did not support this hypothesis. Although the intervention did increase students’ VSSE, it did not significantly affect their STEM grades. This might be due to the fact that post-intervention grades were recorded within

1-2 months after the intervention, which might not be enough time for self-efficacy gains to translate into performance. Another important consideration is that the pre-intervention course grades for 7th graders (the majority of the sample) were pulled from the time period when these students were in a different school building, which might have affected

141 their grades after the transition to the new learning environment. It is also possible that a longer intervention is needed to observe the transfer of VSSE into performance gains.

Not surprisingly, pre-intervention STEM grades were a very strong predictor of post-intervention STEM grades. VS performance also significantly predicted post- intervention STEM grades (with a small effect size), in line with the literature linking visuospatial skills with performance in STEM courses (Ganley & Vasilyeva, 2011).

An interesting finding emerged in relation to students' ethnicity. Particularly, non- white students had better STEM grades than white students (with a small effect size). A more fine-grained descriptive analysis showed that students identified as Asian and

Multi-Racial had higher post-intervention STEM grades than White and American

Indian/Alaskan Native students (whose scores were comparable), followed by Hispanic students and Black/African American students; for pre-intervention STEM grades, Asian students had the highest grades, followed by Multi-Racial students, White students,

Hispanic students, American Indian/Alaskan Native students, and Black/African

American students. This finding is partially consistent with the evidence showing the gap between Hispanic and Black/African American students vs. White students in STEM attainment (Wiswall, Stiefel, Schwartz, & Boccardo, 2014) as well as with the evidence demonstrating that students from certain ethnic groups achieve higher levels of academic performance (Glick & Hohmann-Marriott, 2007). There is not enough data to explain the root of these differences in STEM performance in the current study, but this would be an interesting research direction, especially in relation to visuospatial self-efficacy.

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Contrary to the previous findings about the role of gender in visuospatial performance, gender did not seem to have a significant impact on visuospatial self- efficacy, performance, and STEM grades. Perhaps, because the course in which the intervention took place was an elective, there might have been a self-selection effect at play, meaning that female students chose to be in this course because they initially had a high level of visuospatial competence and self-efficacy.

Limitations and future directions

The results of this study should be viewed within its limitations. First, the schools in which the intervention took place had an emphasis on STEM subjects, and the course in which students engaged with the game was an elective, indicating the possibility of a self-selection effect. Considering this effect and the demographics of the sample

(predominantly white students and mostly male), the results should be generalized with caution. More research is needed to investigate the impact of the intervention in other populations.

Furthermore, the intervention was limited to only 2 weeks. It is possible that a longer intervention could produce different results. It also took place in the classroom, which imposed strict time constraints. Future studies can investigate the impact of the intervention through casual play at home or in extracurricular and other enrichment programs over a longer period of time.

Finally, the current study highlighted several ways to enhance the intervention through (a) scaffolding students’ problem-solving process by modeling communication

143 patterns; (b) taking into account students’ preferences for partners to play the game with;

Conducting the intervention with these improvements may improve the results.

This study was one of the first attempts to conceptualize and empirically analyze visuospatial self-efficacy. While the intervention was effective in increasing VSSE, the lack of connection between VSSE and VS performance and STEM performance was an unexpected outcome. Future studies can investigate this relationship further by focusing on the following questions:

• What is the relationship between VSSE, VS performance and STEM

performance in the long run (e.g., over months or years)?

• How do demographic variables (e.g., gender, ethnicity, socioeconomic

status) mediate/moderate this relationship?

• How do different types of VS interventions compare in developing VSSE?

• What are the limits of VSSE transfer between VS tasks?

Finally, the current study opens up an important discussion about the pedagogically sound, purposeful serious game design to support visuospatial thinking and the development of other skills. Games have been an important part of the culture and society for the last few decades but little research has addressed practical ways to reconcile educational theory, philosophy, game design theory, practical game design considerations and the needs of teachers and learners. It is my hope that the synthesis and critical analysis of these areas along with the practical recommendations I provided in this dissertation will serve as a starting point to develop serious games that match

144 commercial games in quality and engagement while nurturing active and creative problem-solvers who will become the stronghold of the democratic society.

145

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