Predicting Graduation GPA and Study Duration using Machine Learning: A Case Study of College of Industrial Technology Misrata, Libya

Abstract

The current study aims to use different Machine Learning models to predict the graduation Grade Point Average (GPA) and the duration of graduation in terms of semesters, based on a student’s demographic and academic data from the College of Industrial Technology located in Misrata, Libya.   The model takes into consideration the demographic data of students; student's gender and the age at which they joined the college. The GPA and the type of the secondary school and year of graduation, as well as the scores in four basic subjects are also considered as academic data.  The purpose of the study is to enhance the academic management processes through more informed decisions and the provision of timely aid to students likely to fall behind, underperform, or show significant delay in achieving their academic milestones. The research analyses the performance of various algorithms, and offers suggestions for the advancement of support systems intended for academic counsellors in the institution.