Student academic performance prediction model in computer related courses field

Abstract

The phenomenon of student attrition is a pressing issue for higher education institutions globally. Universities aim to maximize their graduation rates, but maintaining a balance between enrollment and graduation has been challenging for decades. It's critical for universities to understand the rates and reasons behind student attrition, as well as when students are most at risk of dropping out, to implement effective strategies to address this issue. Most dropouts occur early in university life, often due to poor academic performance. This independent study aims to use data to identify factors affecting student performance and create a predictive model for their performance in advanced courses. The results will inform institutional policies and strategies to improve faculty-student interactions and increase retention rates. Identifying at-risk students early and creating support pathways are crucial steps toward reducing student attrition.