Several research studies have been showing that personalized gameful solutions can lead to higher engagement and performance. However, personalized gameful design faces two challenges: deciding how to select game elements and activities that are appealing to different users, and deciding how to adapt the experience to each user. In this talk, Gustavo reports on the latest research and his own experience designing personalized gameful solutions. To solve the first challenge (design), he will show how to use the classification of gameful design elements, the gameful design heuristics, and the user types models to create solutions that are appealing to different users. For the second challenge (adaptation), he will discuss strategies for customization (letting the user adjust their experience at will) or personalization (having the system automatically learn about the user and make adjustments).
Keynote presented at Gamification Europe 2020.
3. Types of Personalization
3
User-initiated (customization) System-initiated (personalization)
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
* Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo.
5. Quest Selection in a Gameful Course
5* Business Systems Analysis course taught by Gustavo F. Tondello at the University of Waterloo. 5
6. Skill Tree in a Gameful Course
6* User Interfaces course taught by Gustavo F. Tondello at the University of Waterloo.
* Gustavo F. Tondello and Lennart E. Nacke. A Pilot Study of a Digital Skill Tree in Gameful Education. GamiLearn 2019.
6
7. Image Tagging Platform
77* Gustavo F. Tondello and Lennart E. Nacke. Validation of User Preferences and Effects of Personalized Gamification on
Task Performance. Frontiers in Computer Science 2, 2020.
8. Image Tagging Platform
88* Gustavo F. Tondello and Lennart E. Nacke. Validation of User Preferences and Effects of Personalized Gamification on
Task Performance. Frontiers in Computer Science 2, 2020.
Customized application:
90%
more images
33%
better experience
(compared to the generic version)
9. Article Classification Microtasks
99* Pascal Lessel, Maximilian Altmeyer, Marc Müller, Christian Wolff, Antonio Krüger. Measuring the Effect of “Bottom-Up”
Gamification in a Microtask Setting. Academic Mindtrek 2017.
Customized application:
66%
more tasks completed
(vs fixed gamification)
120%
more tasks completed
(vs without gamification)
10. Enable or Disable Gamification
1010* Pascal Lessel, Maximilian Altmeyer, Lea Verena Schmeer, and Antonio Krüger. 2019. “Enable or Disable Gamification?” –
Analyzing the Impact of Choice in a Gamified Image Tagging Task. CHI 2019.
Results:
76%
more tags generated
(+/− gamification)
36%
more tags generated
(+/− choice, − gamification)
− gamification
− choice
+ gamification
− choice
− gamification
+ choice
+ gamification
+ choice
11. Avatar Customization for Digital Mental
Health Intervention
1111* Max Birk and Regan Mandryk. Improving the Efficacy of Cognitive Training for Digital Mental Health Interventions
Through Avatar Customization: Crowdsourced Quasi-Experimental Study. J Med Internet Res 21(1), 2019.
Customized avatar:
• Increased avatar identification
• Increased engagement with cognitive training (attention bias modification)
• Reduced anxiety after exposure to negative images
12. Personalized Fitness Recommender System
1212* Zhao Zhao, Ali Arya, Rita Orji, Gerry Chan. Effects of a Personalized Fitness Recommender System Using Gamification
and Continuous Player Modeling: System Design and Long-Term Validation Study. JMIR Serious Games 8(4), 2020.
13. Personalized Fitness Recommender System
1313* Zhao Zhao, Ali Arya, Rita Orji, Gerry Chan. Effects of a Personalized Fitness Recommender System Using Gamification
and Continuous Player Modeling: System Design and Long-Term Validation Study. JMIR Serious Games 8(4), 2020.
14. Personalized
Challenges for
Gameful
Smart Cities
1414* Reza Khoshkangini, Giuseppe Valetto, Annapaola Marconi, Marco Pistore. Automatic generation and recommendation
of personalized challenges for gamification. User Modeling and User-Adapted Interaction, 2020.
Procedurally-generated
challenges:
• Higher impact on
sustainable behaviours
• More economical
(improvement vs reward cost)
(in comparison to manually-
generated challenges)
16. What can we personalize?
16
Activities
Game
Elements
Persuasive
Strategies
Difficulty Rewards
* Gustavo F. Tondello. 2019. Dynamic Personalization of Gameful Interactive Systems. PhD Thesis, University of Waterloo.
17. Groups of Gameful Design
Elements
17
Individual
Motivations
Immersion
Progression
External
Motivations
Risk/Reward
Customization
Incentive
Social
Motivations
Socialization
Assistance
Altruism
* Gustavo F. Tondello, Alberto Mora, Lennart E. Nacke. Elements of Gameful Design Emerging from User Preferences.
CHI PLAY 2017.
18. Groups of Gameful Design
Elements
18
Narrative or Story
Levels or
Progression
Challenges
Avatars Rewards or Prizes
Leaderboards
Power-ups or
Booster
Knowledge sharing
20. Recommender Systems
20
Recommendation
User Profile
Demographics
User type
Personality
Items
Activities
Game elements
Strategies
TransactionsUsers x Items
Contexts
Location
Time
… Ratings
Users x Items
Content-based recommender
Collaborative filtering
Context-aware recommender
Hybrid recommender
* Gustavo F. Tondello, Rita Orji, Lennart E. Nacke. Recommender Systems for Personalized Gamification. UMAP 2017 Adjunct.