Emotion-Aware and Context-Driven Mobile Game-Based Learning: A Machine Learning Approach

Authors

DOI:

https://doi.org/10.3991/ijim.v19i21.57247

Keywords:

- Mobile Learning, Gamification, Context, Emotion analysis, Machine Learning,, Adaptation, Game-Based Learning

Abstract


Mobile learning has transformed the way educational content is delivered, enabling learners to access materials anytime and anywhere through portable devices. This flexibility enhances engagement and supports personalized learning experiences. Therefore, this paper proposes a mobile game-based learning (GBL) framework that integrates engaging gaming elements with educational content to promote learner engagement and motivation. By incorporating emotion, eye gaze, and context-driven adaptation through machine learning techniques, the proposed approach aims to enhance personalization and optimize the learning experience. In the experimental study, a small group of learners aged 8–13 engaged sequentially with both non-adaptive and adaptive versions of the educational game. Emotional analysis revealed that 70% of observed responses during the non-adaptive gameplay were negative, including anger (30%), sadness (10%), and disgust (30%). In contrast, 80% of participants reported greater satisfaction with the adaptive version, citing improved engagement as the reason. The experimental group demonstrated a 15% higher improvement in quiz scores and a 20% reduction in task completion time compared to the control group, which showed only a 10% improvement. Experiments conducted in this study demonstrate the effectiveness of emotion- and context-driven adaptation in GBL environments. The results showed that adaptive gameplay significantly reduced negative emotional responses by 50% and improved learner engagement and satisfaction (Cohen’s d > 1.2). It also revealed that the adaptive group outperformed the non-adaptive group in quiz scores and task efficiency, with statistically significant gains and large effect sizes (Cohen’s d > 1.7), highlighting the efficacy of emotion-and context-driven adaptation in GBL environments. Comparative analysis with prior studies confirms that emotion-aware adaptive GBL reduces negative effects by 50% and improves learning outcomes by 15–20%.

Author Biography

Aiman M. Ayyal Awwad, Tafila Technical University, Tafila, Jordan

Aiman Mamdouh Ayyal Awwad is an Associate Professor of Mobile Computing at Tafila Technical University, Jordan. He is currently on sabbatical at the Department of Computer Information Sciences, Higher Colleges of Technology, UAE. He received his B.Sc. in Computer Science from Muta’h University, Jordan, an M.Sc. from the University of Jordan, and a Ph.D. in Computer Science from Graz University of Technology, Austria, in 2017.

Dr. Awwad has held several academic leadership roles, including serving as Chair of the Department of Computer Science and the Department of Information Technology at Tafila Technical University for three years, and as Deputy Dean of the Faculty of Information and Communication Technology for two years. He is an active reviewer for several IEEE international conferences and a technical committee member and reviewer for several prestigious international journals indexed in Scopus.

He has published numerous articles in international journals and conference proceedings. His research interests include mobile cloud computing and applications, image security and privacy protection, security intelligence, and machine learning.

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Published

2025-11-07

How to Cite

Ayyal Awwad, A. M. (2025). Emotion-Aware and Context-Driven Mobile Game-Based Learning: A Machine Learning Approach. International Journal of Interactive Mobile Technologies (iJIM), 19(21), pp. 4–33. https://doi.org/10.3991/ijim.v19i21.57247

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Section

Papers