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Dynamic Personalization of Gameful Interactive Systems

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These are the slides of my Ph.D. thesis oral defence at the University of Waterloo on June 20, 2019.

Gameful design, the process of creating a system with affordances for gameful experiences, can be used to increase user engagement and enjoyment of digital interactive systems. It can also be used to create applications for behaviour change in areas such as health, wellness, education, customer loyalty, and employee management. However, existing research suggests that the qualities of users, such as their personality traits, preferences, or identification with the task, can influence gamification outcomes.

Given how user qualities shape the gameful experience, it is important to understand how to personalize gameful systems. Current evidence suggests that personalized gameful systems can lead to increased user engagement and be more effective in helping users achieve their goals than generic ones. However, to create this kind of system, designers need a specific method to guide them in personalizing the gameful experience to their target audience. To address this need, this thesis proposes a method for personalized gameful design with three steps: (1) classification of user preferences, (2) classification and selection of gameful design elements, and (3) heuristic evaluation of the design.

Furthermore, this thesis describes the design, implementation, and pilot evaluation of a software platform for the study of personalized gameful design. It integrates nine gameful design elements built around a main instrumental task, enabling researchers to observe and study the gameful experience of participants. The platform is flexible so the instrumental task can be changed, game elements can be added or removed, and the level and type of personalization or customization can be controlled. This allows researchers to generate different experimental conditions to study a broad range of research questions.

Our personalized gameful design method provides practical tools and clear guidelines to help designers effectively build personalized gameful systems.

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Dynamic Personalization of Gameful Interactive Systems

  1. 1. Dynamic Personalization of Gameful Interactive Systems Gustavo Fortes Tondello 20 June 2019
  2. 2. Contributions A. Personalized Gameful Design 1. Classification of user preferences 2. Selection of gameful design elements 3. Evaluation of the design B. Platform for the Study of Personalized Gameful Design 2
  3. 3. Personalized Gamification 1. Classification of user preferences 2. Selection of gameful design elements 3. Evaluation of the design 3
  4. 4. Personalized Gameful Design The tailoring of the gameful design elements by the providers to the users based on knowledge about them, to boost the achievement of the goals of the gameful system. 4Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo.
  5. 5. Types of Personalization 5 User-initiated (customization) System-initiated Access Lotteries Boss Battles Because you recently completed a challenge: Unlock restricted areas of the system You can earn amazing rewards in our lottery Test your skills with these highly difficult tasks
  6. 6. Why Personalize Gameful Systems? • Higher engagement, performance, enjoyment • Boost the achievement of goals • task completion rate, learning, health, employee engagement… 6 Education Health Fitness Nutrition Training Customer relations Human resources Team management Image source: www.pexels.com
  7. 7. Why Personalize Gameful Systems? “I feel like it did [influence my enjoyment] because I felt like I kind of created my own game that was perfect for me and so it felt like I was in control and added to my enjoyment.” 9 Pilot study with N = 50 MTurk participants. Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo.
  8. 8. Personalized Gameful Design 10 Classification of User Preferences Selection of Gameful Design Elements Evaluation of the Design 1 2 3
  9. 9. 1. Classification of User Preferences 11 Classification of User Preferences Selection of Gameful Design Elements Evaluation of the Design 1 2 3
  10. 10. Gamification User Types Hexad Do all users equally enjoy all game elements? 12Andrzej Marczewski. 2015. User Types. In Even Ninja Monkeys Like to Play: Gamification, Game Thinking & Motivational Design. Gamified UK. 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design
  11. 11. Gamification User Types Hexad 24-item scale (developed in collaboration with Andrzej Marczewski and the Austrian Institute of Technology) 13 Image source: screenshots from https://gamified.uk/ G. F. Tondello, R. R. Wehbe, L. Diamond, M. Busch, A. Marczewski, L. E. Nacke. 2016. The Gamification User Types Hexad Scale. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’16, 229–243. 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design
  12. 12. User Types Hexad: Validation Four studies 1. Initial validation in English (N = 133) 2. Large-scale validation in English and Spanish (N = 556) (data gently provided by Alberto Mora) 3. Large-scale validation in English and Spanish (N = 1,328) (data gently provided by Andrzej Marczewski) 4. Validation of suggested improvements in English (N = 152) (data collected in collaboration with Andrzej Marczewski) 14 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design [1] G. F. Tondello, R. R. Wehbe, L. Diamond, M. Busch, A. Marczewski, L. E. Nacke. 2016. The Gamification User Types Hexad Scale. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’16, 229–243. [2-4] G. F. Tondello, A. Mora, A. Marczewski, L. E. Nacke. 2019. Empirical Validation of the Gamification User Types Hexad Scale in English and Spanish. International Journal of Human-Computer Studies 127, 95–111.
  13. 13. User Types Hexad: Validation Analysis methods • Exploratory Factor Analysis (EFA) • Confirmatory Factor Analysis (CFA) with structural equation modeling (SEM) Results The scale is generally consistent and reliable [1] has already been cited 112 times 15 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design [1] G. F. Tondello, R. R. Wehbe, L. Diamond, M. Busch, A. Marczewski, L. E. Nacke. 2016. The Gamification User Types Hexad Scale. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’16, 229–243. [2-4] G. F. Tondello, A. Mora, A. Marczewski, L. E. Nacke. 2018. Empirical Validation of the Gamification User Types Hexad Scale in English and Spanish. International Journal of Human-Computer Studies 127, 95–111.
  14. 14. 2. Selection of Gameful Design Elements 18 Classification of User Preferences Selection of Gameful Design Elements Evaluation of the Design 1 2 3
  15. 15. Groups of Gameful Design Elements 19 Individual Motivations Immersion Progression External Motivations Risk/Reward Customization Incentive Social Motivations Socialization Assistance Altruism 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design Results of Principal Components Analysis (N = 196) Image sources: www.pexels.com and Game-icons.net Gustavo F. Tondello, Alberto Mora, and Lennart E. Nacke. 2017. Elements of Gameful Design Emerging from User Preferences. In Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’17, 129–142.
  16. 16. Groups of Gameful Design Elements 28 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Immersion Progression Customization Incentive Risk/Reward Altruism Socialization Assistance Note: Mean Likert scores (1–5) per group (N = 196) Gustavo F. Tondello, Alberto Mora, and Lennart E. Nacke. 2017. Elements of Gameful Design Emerging from User Preferences. In Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’17, 129–142. 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design
  17. 17. Preferences by Gender 29 Note: Mean differences per gender and group (N = 124 men, 53 women) Gustavo F. Tondello, Alberto Mora, and Lennart E. Nacke. 2017. Elements of Gameful Design Emerging from User Preferences. In Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’17, 129–142. 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design 0.2 -0.29 -0.08 -0.14 0.13 -0.11 -0.37 0.63 0.37 0.31 -0.25 0.23 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Socialization Assistance Immersion Customization Altruism Incentives Men Women
  18. 18. Preferences by Hexad User Type 30 Note: Based on results from correlation analysis (N = 196) Gustavo F. Tondello, Alberto Mora, and Lennart E. Nacke. 2017. Elements of Gameful Design Emerging from User Preferences. In Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY ’17, 129–142. 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design Participants who scored higher as… …tended to prefer these groups Free Spirit Immersion Philanthropist Immersion, Progression, Altruism Achiever Socialization, Immersion, Risk/Reward, Progression, Altruism Socialiser Socialization, Assistance, Altruism Player Socialization, Risk/Reward, Incentive Disruptor Immersion, Risk/Reward
  19. 19. Personalized Gameful Design 31 Classification of User Preferences Selection of Gameful Design Elements Evaluation of the Design 1 2 3
  20. 20. 3. Evaluation of the Design 32 Classification of User Preferences Selection of Gameful Design Elements Evaluation of the Design 1 2 3
  21. 21. Gameful Design Heuristics 33 G. F. Tondello, D. L. Kappen, E. D. Mekler, M. Ganaba, L. E. Nacke. 2016. Heuristic Evaluation for Gameful Design. In Proceedings of CHI PLAY ’16 Extended Abstracts. G. F. Tondello, D. L. Kappen, M. Ganaba, L. E. Nacke. 2019. Gameful Design Heuristics: A Gamification Inspection Tool. In Proceedings of HCI International 2019. Infographic by Dennis Kappen and Marim Ganaba Heuristics General design principle or guidelines Heuristic Evaluation Use of said principles to identify design problems Gameful Design Heuristics Set of guidelines for heuristic evaluation of gameful applications 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design
  22. 22. Gameful Design Heuristics How to conduct a heuristic evaluation of a gameful system 1. Familiarize yourself with the application 2. Use the heuristics checklist 3. For each heuristic: a. Familiarize yourself with the heuristic b. Think about the supporting questions in relation to the app c. If you identify any issue in the app related to the heuristic, write it down 4. Finally, count the number of issues identified for each category to identify those with more issues 34 Gustavo F. Tondello, Dennis L. Kappen, Elisa D. Mekler, Marim Ganaba, and Lennart E. Nacke. 2016. Heuristic Evaluation for Gameful Design. In Proceedings of CHI PLAY ’16 Extended Abstracts, 315–323. G. F. Tondello, D. L. Kappen, M. Ganaba, L. E. Nacke. 2019. Gameful Design Heuristics: A Gamification Inspection Tool. In Proceedings of HCI International 2019. 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design
  23. 23. Usefulness of this evaluation method 35 1. Classification of User Preferences 2. Selection of Gameful Design Elements 3. Evaluation of the Design Note: N = 2 without heuristics. N = 3 with heuristics for Habitica, 1 for Termling. G. F. Tondello, D. L. Kappen, M. Ganaba, L. E. Nacke. 2019. Gameful Design Heuristics: A Gamification Inspection Tool. In Proceedings of HCI International 2019. Results • Gameful design heuristics help evaluators who are not familiar with gamification to evaluate a system as well as a gamification expert who does not use the heuristics • Gameful design heuristics improves the ability of gamification experts to perform an heuristic evaluation 7 8 13 24 0 5 10 15 20 25 30 Habitica Termling Number of motivational issues found Without Heuristics With Heuristics
  24. 24. A Platform for the Study of Personalized Gameful Design 36
  25. 25. Image Classification Task 37
  26. 26. Game Elements 38
  27. 27. Game Elements (examples) 39 Unlockable Content Power Ups Leaderboard Levels
  28. 28. Frequency of Element Selection 36 30 30 23 23 20 16 16 6 0 5 10 15 20 25 30 35 40 Progress Feedback Levels Power-ups Leaderboards Chance Badges Unlockable Content Challenges Moderating Role 40 Pilot study with N = 50 MTurk participants. Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo.
  29. 29. Rating of the Experience 34 36 42 39 39 9 8 6 5 11 7 1 2 3 0 0 5 10 15 20 25 30 35 40 45 Overall experience Element selection Satisfaction Preference matching Enjoyment Positive Neutral Negative 41 Pilot study with N = 50 MTurk participants. Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo.
  30. 30. Summary of Contributions 42
  31. 31. Personalized Gameful Design 43Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo. The tailoring of the gameful design elements by the providers to the users based on knowledge about them, to boost the achievement of the goals of the gameful system.
  32. 32. Personalized Gameful Design 44 Classification of User Preferences Selection of Gameful Design Elements Evaluation of the Design 1 2 3
  33. 33. A Platform for the Study of Personalized Gameful Design 45
  34. 34. Future Work 46
  35. 35. Open Research Questions Do user preferences vary by context or country? Do users’ behaviours correspond to their self-reported preferences? Do different designs of gameful elements influence user preferences? What other evaluation methods can be devised? 47
  36. 36. Empirical Studies of Personalized Gameful Systems Personalized vs Generic systems Partial vs Full Personalization Personalization vs Customization 48
  37. 37. Friend invite Social discovery Trading Access Lotteries Boss Battles Because you recently completed a challenge: Because you recently joined a team: Unlock restricted areas of the system You can earn amazing rewards in our lottery Test your skills with this highly difficult tasks Invite your friends to work with you Find other users with similar interests Exchange your spare items with other users Recommender Systems for Personalized Gamification 49 Models of user types and design elements Recommendation algorithms Machine learning
  38. 38. What else can we personalize? Activities Game Elements Persuasive Strategies Difficulty Rewards 50Image source: www.pexels.com
  39. 39. Questions and Discussion 51 Dynamic Personalization of Gameful Interactive Systems Gustavo Fortes Tondello Acknowledgments: This research was supported by: • The National Council for Scientific and Technological Development – Brazil (CNPq) • University of Waterloo (Cheriton School of Computer Science, Games Institute, HCI Games Group) • NSERC (grants RGPIN-418622-2012; RGPIN-2018-06576) • SSHRC (grant 895-2011-1014, IMMERSe) • Mitacs (grant IT07255) • CFI (grant 35819) • NSERC CREATE SWaGUR

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