PERSONALIZED STUDENT MODELING VIA HIERARCHICAL BAYESIAN NEURAL NETWORKS WITH CONCEPT GRAPHS

Personalized education systems require sophisticated student modeling approaches that can capture individual learning patterns, knowledge states, and cognitive processes across diverse educational domains. Traditional student modeling techniques struggle to represent the complex relationships between learning concepts while accounting for individual differences in learning progression and knowledge acquisition patterns. The challenge lies in developing models that can simultaneously capture hierarchical knowledge structures, individual learning trajectories, and uncertainty in student knowledge assessment. This study proposes a novel framework that integrates Hierarchical Bayesian Neural Networks (HBNNs) with concept graphs to create comprehensive personalized student models capable of representing both individual learning characteristics and domain knowledge structures. The framework employs probabilistic modeling to capture uncertainty in knowledge assessment while concept graphs provide structured representations of learning dependencies and prerequisite relationships. The hierarchical Bayesian approach enables effective personalization by modeling individual student parameters within broader population distributions while maintaining computational efficiency for real-time educational applications. Experimental evaluation using large-scale educational datasets demonstrates that the proposed framework achieves 34% improvement in knowledge state prediction accuracy compared to traditional student modeling approaches. The integration of concept graphs with Bayesian neural networks results in 42% better performance in learning outcome prediction and 38% improvement in personalized recommendation effectiveness. The framework successfully captures individual learning patterns while maintaining interpretability for educational practitioners and adaptive learning system designers.