Fast E-Learning Recommendation: Enhancing Model Efficiency with Q-Matrix Complexity Reduction

Abstract

Intelligent tutoring systems generate a large volume of data, which becomes particularly valuable when effectively leveraged for learner performance prediction in adaptive learning environments. In this context, the speed and predictive accuracy of machine learning models are crucial, as they determine the system’s ability to deliver timely and relevant insights and support responsive, personalized instruction. Enhancing model speed not only increases tutoring efficiency but also improves the adaptability of educational systems to learners’ needs. This study introduces an approach aimed at improving the execution time of three logistic regression-based models widely used for learner performance prediction: DAS3H (Item Difficulty, Student Ability, Skill, and Student Skill Practice History), AFM (Additive Factor Model), and PFA (Performance Factor Analysis). The proposed optimization reduces the complexity of the Q-matrix that links each item to its required knowledge components by simplifying its structure while preserving pedagogical relevance. An empirical evaluation was conducted on four real-world datasets collected from online tutoring platforms. The results demonstrate that the proposed approach, called Fast E-learning Recommendation (FER), significantly improves the execution speed of the three models while maintaining comparable predictive performance across datasets.