Predicting Undergraduate Level Students’ Performance Using Regression

Abstract

Students’ academic performance in the university environment changes from one academic year to another as they climb up the ladder of their academic programme. Predicting students’ academic performance in higher educational institutions is challenging due to the lack of a central database of students’ performance records. The other challenge is the lack of standard methods for predicting students’ performance and other moderating factors like physical, economic and health that affect students’ progress. In this work, we predicted students’ performance based on previous academic results. A model to predict students’ performance based on their Cumulative Grade Point Average (CGPA) was developed using Linear Regression Algorithm. A dataset of 70 undergraduate students studying Computer Science was analyzed and the results show that the model was able to predict the 4th year CGPA of the Students using the previous Cumulative Grade Point of the past three years with an accuracy of 87.84%, and a correlation of 0.9338. This study also identified students’ second semester CGPA in the first year and their first semester CGPA in the second year as the most important CGPAs that affect the accuracy