Prediction of Student Academic Performance Utilizing a Multi-Model Fusion Approach in the Realm of Machine Learning

Abstract

The digitization of college student management is a crucial approach for training institutions to decrease management costs while enhancing the quality of students’ development. In this study, we focused on the students majoring in Computer Science in a certain university and conducted an exploration using their scores in multiple undergraduate courses. Initially, we selected the students’ basic and core academic courses based on the training program and identified four groups of course combinations with strong positive correlations through correlation and cluster analysis. This finding helped the university optimize the arrangement and structure of the Computer Science major’s course system. Next, we organized the student overall course performance data in a sequential format based on the semester order. Multiple machine learning models were utilized to perform regression prediction for student performance and classification prediction tasks to determine the student’s performance level. Finally, we integrated multiple machine learning models to create a practical framework for predicting student academic performance, which can be applied in student digital management. The framework can also provide effective decision support for academic early warning and guide the students’ development.