Development of MyCGPA for Early Predicting Students’ Academic Performance

Abstract

One of the primary concerns in higher education is the early identification of underperforming students. To address this issue, the current study proposes the development of a system that would assist academic advisers and faculty management to identifying students at risk of low academic performance at an early stage. This system utilises a prediction model based on a dataset of academic and demographic data from the UPNM’s Computer Science students. The dataset contains information from 97 students and 21 characteristics. We developed a prediction model for Cumulative Grade Point Average (CGPA) using the regression technique, focusing on three variables: 'activity', 'absence', and 'GPA'. The prototype model was used in the system development process. The study's findings are valuable for the institution (university), since they enable for the early identification of those who may struggle academically. Future enhancements include increasing the dataset and using more powerful algorithms to predict kids' academic achievement.

Keywords

• predictive model
• early prediction
• machine learning
• academic performance
• regression