Actor-Critic Reinforcement Learning for Personalized STEM Learning Path Optimization - Hatta, Muhammad; Magdalena, Lena; Putra, Dwi Pasha Anggara; Runa, Yohanes Michael Fouk; Irfansyah, Ananda; Valentino, Fernando (Journal of Information Systems and Informatics, 2025)
This study addresses the critical need for adaptive learning in non-formal education settings, particularly Community Learning Centres (PKBM) in Indonesia, where student heterogeneity and limited resources challenge conventional teaching methods. We developed a personalized learning path optimization model using Actor-Critic Reinforcement Learning (RL) to enhance STEM competency development. The novel framework integrates cognitive, affective, and personality features to dynamically adjust material difficulty based on real-time analysis of student cognitive states (quiz performance, completion rate) and affective conditions (emotional level), moving beyond static predictive approaches. Experimental results on a synthetic dataset demonstrate that the Actor-Critic agent achieves statistically significant higher rewards (-2.92 vs -3.01, p
Keywords: Adaptive Learning, Reinforcement Learning, STEM Competency, Personalized Learning Paths, Non-Formal Education