A Learning Resource Recommendation Algorithm Based on Online Learning Behavior

Haoxin Xu, Bihao Hu, Xiaoqing Gu, Longwei Zheng

Abstract: Faced with abundant online course resources, learners struggle to choose suitable materials. Learning resource recommendation algorithms can help address this. Rich online learning behavior data enables such recommendations. However, existing research only uses behavior events as learner features, ignoring event order i.e. Learning Behavior Patterns (LBPs). Also, only using click counts loses valuable information, hurting performance. We propose an algorithm leveraging online behavior sequences. First, extract sequences from logs and generate LBPs. Next, calculate Term Frequency Inverse Document Frequency (TF-IDF) values for each LBP as feature vectors. Cluster learners to improve efficiency. Finally, Calculate intra-cluster similarities for collaborative filtering recommendations. Experiments show over 30% precision, 9% recall, and 10% F1 improvements versus existing methods. Further ablation indicates learner clustering boosts time efficiency 3.75x without performance impact. Using TF-IDF values and tuning LBP length significantly improves performance. Overall, modeling orders via LBPs and better features like TF-IDF give major gains.