Gamified systems : exploring state-of-the-art design beyond points and badges
Ren, Xinyao (2025)
Kandidaatintyö
Ren, Xinyao
2025
School of Engineering Science, Tietotekniikka
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025060358166
https://urn.fi/URN:NBN:fi-fe2025060358166
Tiivistelmä
In recent years, the rapid development of gamification systems has brought increasing attention to the limitations of the traditional Points-Badges-Leaderboards model. This study introduces a personalized gamification system grounded in psychological theory and user typologies. Drawing on frameworks such as Self-Determination Theory, Flow Theory, Learning Engagement, and Learning Experience Design, the system is designed to meet users' core psychological needs—autonomy, competence, and relatedness—to foster more sustainable learning behaviors.
Combining an extensive literature review with qualitative interviews based on the BrainHex player model, which categorizes users into seven distinct types, the research analyzes how different user archetypes respond to gamified systems. The findings confirm the limitations of externally reward-driven designs and emphasize the importance of responsive, user-centered strategies in gamification.
Based on these insights, the study develops three tailored interface modes—Challenge & Growth, Exploration & Adventure, and Social & Competitive—each offered in both high- and low-gamification versions to accommodate user preferences and cognitive load. A simplified demonstration (demo) of the low-gamification version is also presented to showcase how greater user autonomy can be achieved. Key features of the system include adaptive task structures, AI-powered content recommendation, and customizable learning pathways. Together, these elements create a flexible, engaging, and personalized learning experience that supports sustained motivation over time.
Combining an extensive literature review with qualitative interviews based on the BrainHex player model, which categorizes users into seven distinct types, the research analyzes how different user archetypes respond to gamified systems. The findings confirm the limitations of externally reward-driven designs and emphasize the importance of responsive, user-centered strategies in gamification.
Based on these insights, the study develops three tailored interface modes—Challenge & Growth, Exploration & Adventure, and Social & Competitive—each offered in both high- and low-gamification versions to accommodate user preferences and cognitive load. A simplified demonstration (demo) of the low-gamification version is also presented to showcase how greater user autonomy can be achieved. Key features of the system include adaptive task structures, AI-powered content recommendation, and customizable learning pathways. Together, these elements create a flexible, engaging, and personalized learning experience that supports sustained motivation over time.