Proficiency Modeling in Junior High Math: Adapted Cognitive Statistical Models to E-Book Learning Contexts

Abstract

Digital learning platforms equipped with behavior sensors have provided abundant educational data. Utilizing this data, learner modeling can identify assorted learner characteristics from their behavior logs for learning analytics and dynamically update them in real time. There is a growing demand for knowledge-level modeling, moving beyond behavioral logs to assess knowledge proficiency. Based on item response theories and cognitive statistical models, existing studies estimate learning rates across various knowledge elements in each learning step in intelligent tutoring systems. However, these models, tailored to specific knowledge domains, offer limited flexibility across different knowledge units and scenarios. This paper introduces an adaptation of the basic additive factor model underpinned by logistic regression, focusing on behavior indicators. Drawing upon authentic learning data from an e-book learning infrastructure for junior high math, we examine the feasibility of our adapted models and demonstrate their potential for flexibility across knowledge units and learning phases.