Learner Cognitive Feature Model for Learning Resource Personalizing Recommendation

Abstract

In this paper, we propose a novel learner cognitive feature model for personalized guidance and push (LCFLM) that traces the evolution of learners’ knowledge proficiency based on their exercising logs in online learning systems. Specifically, we introduce the exercise-aware dependency hierarchical graph of exercise dependency and pattern dependency that can establish a model of exercise dependency relationships. Additionally, we propose the implementation of a forget gating mechanism, which combines the forgetting features with the knowledge state features to predict a student’s learning performance. The experimental results clearly demonstrate that LCFLM achieves the new state-of-the-art performance, exhibiting an improvement of at least 3% in both AUC and ACC. Furthermore, the LCFLM model has the ability to autonomously uncover the fundamental concepts underlying exercises and provides a visual representation of a student’s evolving knowledge state.