Study on Predicting University Student Performance Based on Course Correlation

Abstract

Performance prediction has proven to be an effective method for monitoring learning progress, managing student performance, and enhancing teaching quality. In an effort to analyze and predict students' grades in colleges and universities, a comprehensive database of student information is utilized along with big data technology to mine the correlation between courses. To achieve this, a student performance prediction model (SPCA) based on course association is proposed. The model selects 29 course grades from industrial engineering students in a particular school's class of 2018-2020. The courses are then clustered into three categories: mathematical computation, general and professional fundamentals, and practical application. This clustering is accomplished using the Self-Organizing Map (SOM) algorithm. Subsequently, the Apriori algorithm is employed to mine association rules among the courses. Finally, a decision tree algorithm is utilized to predict the grades of previous courses within the same category, based on the association rules discovered. The outcomes of this research can optimize course scheduling, assist students in planning their study plans, and provide practical reference value for improving teaching quality and teaching management.