FER-XGBoost: A Hybrid Approach for Fast and Accurate Learner Performance Prediction

Abstract

In adaptive learning environments, accurate prediction of learner performance is essential for delivering personalized educational support. Logistic regression-based models such as PFA, AFM, and DAS3H have demonstrated strong predictive capabilities; however, they face two complementary limitations: high computational cost due to Q-matrix density and a predictive accuracy ceiling inherent to their linear formulation. This study proposes a hybrid algorithm that addresses both limitations simultaneously. The Fast E-learning Recommendation (FER) method is first applied to reduce Q-matrix complexity by aggregating all knowledge components associated with each item into a single combined skill. From an algorithmic perspective, this transformation reduces training time complexity from OI×K to O(I), where I is the number of items and K is the number of knowledge components, yielding substantial computational savings while preserving the conjunctive cognitive structure of the original Q-matrix. XGBoost is then integrated into the simplified models to enhance predictive performance by capturing non-linear relationships that logistic regression cannot capture. The proposed hybrid framework is evaluated on four real-world educational datasets. Experimental results demonstrate that the FER-XGBoost approach achieves a favorable balance between computational efficiency and predictive accuracy: in several configurations, it not only maintains but also slightly improves predictive performance while significantly reducing execution time compared to standard baseline models.