Actor-Critic Reinforcement Learning for Personalized STEM Learning Path Optimization

Muhammad Hatta; Lena Magdalena; Dwi Pasha Anggara Putra; Yohanes Michael Fouk Runa; Ananda Irfansyah; Fernando Valentino

doi:10.51519/journalisi.v7i3.1270

Muhammad Hatta Universitas Catur Insan Cendekia, Indonesia
Lena Magdalena Universitas Catur Insan Cendekia, Indonesia
Dwi Pasha Anggara Putra Universitas Catur Insan Cendekia, Indonesia
Yohanes Michael Fouk Runa Universitas Catur Insan Cendekia, Indonesia
Ananda Irfansyah Universitas Catur Insan Cendekia, Indonesia
Fernando Valentino Universitas Catur Insan Cendekia, Indonesia

DOI: 10.51519/journalisi.v7i3.1270

Keywords: Adaptive Learning, Reinforcement Learning, STEM Competency, Personalized Learning Paths, Non-Formal Education

Abstract

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<0.05) and greater output stability compared to a random baseline. Although the absolute reward difference is modest, it reflects more consistent adaptive policy performance, despite limited effect size (Cohen's d=0.0317). Feature importance analysis confirms that quiz_score and emotion_level are the dominant factors influencing adaptive recommendations, while personality traits show negligible impact. The framework offers a viable pathway for scalable, personalized learning in resource-constrained environments. Future work should validate the model with real-world student data and refine reward functions to strengthen practical impact.

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