What question did this study set out to answer?

This research aims to improve early identification of students at risk of academic failure using machine learning methods.

May 1, 2026Open Access

AI-Driven Approach To Student Performance Analysis System

Key Points

This research aims to improve early identification of students at risk of academic failure using machine learning methods.
Analyzed student data including attendance, past grades, and family background.
Developed a predictive model using machine learning techniques to identify at-risk students.
Evaluated the model's accuracy and effectiveness in a real-world educational setting.
The predictive model achieved over 90% accuracy in identifying at-risk students.
Early intervention based on predictions could prevent academic failure.
Teachers received timely alerts to assist struggling students before report cards were issued.

Abstract

There's a lot more to student performance than just marks. Some kids barely pass written exams but shine in group projects or sports. The problem is, most colleges still judge students almost entirely by their test scores. That's like judging a fish by its ability to climb a tree. By the time a teacher realizes someone's struggling, that student might already be failing or even thinking of dropping out. So what if we could spot trouble earlier — way before the report card says it all? That's what this paper is about. We used machine learning to sift through student data — attendance, past grades, even family background — and predict who might fall behind. Not just for the sake of prediction, but to actually give teachers a heads-up so they can step in and help. The results were pretty solid. Our model caught most at-risk students with over 90% accuracy. Not perfect, but a lot better than waiting till the end of the semester.

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