Demystifying Student Learning Behaviors: An Explainable AI Perspective in Educational Analytics

Abstract

Understanding student learning behaviors is crucial for improving educational outcomes, enabling personalized instruction, and fostering effective learning environments. This study presents an approach to decode and analyze student behaviors using Explainable Artificial Intelligence (XAI) methods integrated into educational analytics systems. Traditional black-box machine learning models often provide high predictive accuracy but lack transparency, making it difficult for educators to trust and act upon the insights generated. To address this challenge, we propose a framework that employs explainable models, including SHAP, LIME, and attention-based neural networks, to identify key cognitive and behavioral indicators influencing student performance. Using real-world educational datasets, this research demonstrates how XAI enhances interpretability while maintaining competitive prediction accuracy. The findings indicate that explainable models not only uncover hidden patterns in student learning processes but also empower educators with actionable insights to design personalized interventions and adaptive learning pathways.