LLM-EPSP: Large language model empowered early prediction of student performance

Abstract

Early prediction of student performance (EPSP) has garnered significant attention due to its educational value, especially its importance in academic early warning systems. State-of-the-art data mining methods have achieved remarkable success by optimizing feature selection and model enhancements. However, these methods often face challenges, including the cold-start problem, limited exploration of the intrinsic relationships among features, and poor generalization. In this work, we explore the utilization of Large Language Models (LLMs) as information integrators to address these challenges and propose a novel model called Large Language Model Empowered Early Prediction of Student Performance (LLM-EPSP). Specifically, for the cold-start problem, LLM-EPSP benefits from the inherent advantages of LLMs, which stem from their extensive pretraining on diverse datasets. This enables the model to make informed predictions even with limited initial data. For exploring intrinsic relationships among features, LLM-EPSP employs feature fusion techniques to uncover underlying connections between various features, ensuring a comprehensive and robust analysis. To enhance the generalization capabilities of LLM-EPSP, we develop predefined templates that facilitate its adaptation to a wide range of educational contexts. We evaluate our method on two real-world datasets: (1) OULAD, which includes data on 22 courses and 32,593 students, and (2) the UCI Machine Learning Repository, which contains 23 types of features from 649 students. Extensive validation demonstrates that LLM-EPSP considerably outperforms baseline approaches across diverse scenarios. Further analysis results also demonstrate the robustness and versatility of LLM-EPSP, suggesting its enormous potential in practical applications.