Citation

Shabadurai, Yogeswari and Chua, Fang Fang and Lim, Tek Yong (2025) A Reinforcement Learning-Based Dynamic Adaptive Gamification (DAG) Method for Online Employee Training. In: 14th International Conference on Information Technology in Asia, CITA 2025, 5 August 2025 - 6 August 2025, Kota Samarahan.

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Abstract

In times where working remotely and online work environments are on the rise; the traditional employee training method struggles to stay motivated and engaged. Gamification has emerged as a widely used motivational approach that can be tailored to user preferences to improve user engagement. While various machine learning techniques have been applied to gamification, the use of reinforcement learning for real-time adaptive personalization in employee training contexts remains comparatively underutilized. Therefore, this study presents a Reinforcement Learning-Based Dynamic Adaptive Gamification (DAG) method designed to enhance online employee training through real-time adaptation and personalization. The proposed approach leverages data-driven decision making, iterative feedback mechanisms, and personalization algorithms to optimize the training experience. The methodology comprises three key phases: (A)System design, (B)Implementation consisting the Adaptation and Personalization processes such as game interaction, ongoing feedback and (C)Evaluation. The results indicate the effectiveness of the DAG method in improving user engagement and increasing user retention in online training environment.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Adaptive, engagement, gamification, personalization, reinforcement learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	02 Dec 2025 08:28
Last Modified:	02 Dec 2025 08:28
URII:	http://shdl.mmu.edu.my/id/eprint/14938

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