Predicting Student's Performance Through Data Mining

TL;DR

This paper explores using data mining and machine learning on LMS data to predict student performance early, aiming to identify strengths and weaknesses to improve educational outcomes.

Contribution

It introduces a method for predicting student performance using LMS data with machine learning, providing insights for early intervention.

Findings

Machine learning models can predict student performance with reasonable accuracy.

LMS data reveals behavioral patterns linked to exam success or failure.

Early prediction can help tailor educational support for students.

Abstract

Predicting the performance of students early and as accurately as possible is one of the biggest challenges of educational institutions. Analyzing the performance of students early can help in finding the strengths and weakness of students and help the perform better in examinations. Using machine learning the student's performance can be predicted with the help of students' data collected from Learning Management Systems (LMS). The data collected from LMSs can provide insights about student's behavior that will result in good or bad performance in examinations which then can be studied and used in helping students performing poorly in examinations to perform better.