Enhancing knowledge point recommendation in intelligent tutoring systems using global negative sample weighting

The aim of this study is to address data sparsity, popularity bias, and insufficient diversity in knowledge point recommendation algorithms within intelligent tutoring systems. This study proposes a new model that dynamically adjusts negative sample probability and weighting based on global frequency and pedagogical difficulty to enhance recommendation precision and equity, especially for underrepresented or complex knowledge points. The study employs a publicly available dataset comprising 6,607 student records and 20 distinct variables. Without explicit curricular tags, knowledge points were operationalized as latent learning units identified through unsupervised clustering. Specifically, seven key performance and behavioral indicators, Hours_Studied, Attendance, Previous_Scores, Sleep_Hours, Tutoring_Sessions, Physical_Activity, and Exam_Score were selected as features for clustering. These features were first standardized using Z-score normalization. Subsequently, the standardized features were partitioned into ten clusters using the K-Means algorithm with a random_state of 42 to ensure reproducibility. The frequency of a knowledge point was defined as its relative prevalence within the dataset. The difficulty of each knowledge point was inferred from aggregated student performance within the corresponding cluster. Results certify achieving NDCG of 0.95, HRA10 of 1.00, and AUC of 0.96.