Student Performance Prediction Approach Based on Educational Data Mining

Predicting student performance is crucial for improving students’ future academic achievements. Within student groups, common characteristics can reveal trends in overall student learning. Most studies tend to focus on the common characteristics of students but ignore their individual characteristics. However, individual characteristics are important in promoting student academic performance because they allow us to understand the unique learning performances of each student. To address this issue, this paper proposes a student performance prediction approach. First, addressing the problem of difficulty in effectively dividing the student samples under multi-dimensional discrete data, we propose a method that combines the relationship matrix-based bipartite network approach (RMBN) with Louvain clustering. Second, the hybrid neural network model based on a relationship matrix (RMHNN) is proposed to address the problem that discrete types of features are difficult to fit by algorithms. The results show that the implementation of the model on real student data can effectively predict student performance with an accuracy of 93.1% and an F1-score of 90.45%. With the model’s predicted student performance, educators can provide individualized support and assistance to each student.