Citation

Shabadurai, Yogeswari (2026) Dynamic adaptive gamification method for improving user engagement in online training environments. PhD thesis, Multimedia University.

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Abstract

User engagement remains a critical challenge in online training environments, where motivation and participation often decline over time. Although gamification has been widely adopted to improve engagement, most existing systems rely on static gamification approaches in which player types and game elements remain fixed throughout the learning process. Such approaches assume that user motivations are constant, limiting their ability to respond to evolving learner behaviours and sustain long-term engagement. To address this limitation, this thesis proposes a Dynamic Adaptive Gamification (DAG) method that personalizes gamification elements in real time based on evolving user behaviour and interaction patterns. The research focused on identifying adaptive gamification strategies, modelling dynamic player behaviour, developing the DAG method, and evaluating its effectiveness in improving engagement. A taxonomy of 40 adaptive gamification strategies grouped into six motivational themes was established, while a dynamic correlation mechanism was developed to continuously update player traits as evolving motivational vectors. The DAG method was implemented and validated through simulation and prototype-based evaluation. Simulation results demonstrated strong convergence behaviour, with prediction accuracy improving from approximately 65% to 95% across learning iterations. A comparative evaluation involving 60 participants compared the Dynamic Adaptive Gamification Group (DAGG) with the Static Gamification Group (SGG). The results showed that the DAG method significantly improved user engagement, including higher session duration, interaction frequency, task completion, and retention rates, supported by statistically significant findings and large effect sizes. The findings confirm that the DAG method provides more accurate personalization and more effective engagement enhancement compared to static gamification approaches. This research contributes to adaptive gamification by introducing a dynamic behaviour-driven personalization method that continuously adapts game elements according to evolving user motivations and interaction feedback. The proposed DAG method provides a scalable foundation for intelligent and personalized online learning and training environments capable of sustaining long-term user engagement.

Item Type:	Thesis (PhD)
Additional Information:	Call No.: T57.92 .S53 2026
Uncontrolled Keywords:	Gamification—Industrial applications
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	05 Oct 2026 06:38
Last Modified:	05 Oct 2026 06:38
URII:	http://shdl.mmu.edu.my/id/eprint/16882

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