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Selecting ML/DL Algorithms for Gamification of Formative Assessment: A Framework and Analysis
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Author(s): Arif Husen (Virtual University of Pakistan, Lahore, Pakistan), Neelam Alam (Virtual University of Pakistan, Lahore, Pakistan), Muhammad Hasanain Chaudary (COMSATS University of Islamabad, Lahore, Pakistan), Shabib Aftab (Virtual University of Pakistan, Lahore, Pakistan)and Syed Shah Muhammad (Virtual University of Pakistan, Lahore, Pakistan)
Copyright: 2026
Pages: 52
Source title:
AI-Powered Educational Games and Simulations
Source Author(s)/Editor(s): Saima Munawar (Department of Computer Science and Information Technology, Virtual University of Pakistan, Pakistan)and Nasir Naveed (Department of Computer Science and Information Technology, Virtual University of Pakistan, Pakistan)
DOI: 10.4018/979-8-3373-0035-1.ch002
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Abstract
Gamification in formative assessment is a promising approach to enhance student engagement and learning in AI-era education. Machine learning enables personalized experiences and adaptive feedback, with advancements like Context-Aware ML, explainable ML, and self-attention models playing key roles. Determining the best approach for gamification requires careful analysis, as factors like algorithm transparency, adaptability, and educational complexity remain challenges. While reinforcement learning and collaborative filtering have been explored, their application in gamification lacks integration with recent ML developments. Hybrid models that combine multiple algorithms, empirical studies to evaluate effectiveness, and iterative refinement using user data offer potential solutions. This chapter will address these challenges and propose a framework (GOFA) for selecting optimal ML algorithms to enhance gamification strategies in formative assessments.
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