Mining Educational Data to Analyze Students' Performance

TL;DR

This paper demonstrates how data mining, specifically decision tree classification, can be used in higher education to predict student performance, identify at-risk students, and improve educational quality.

Contribution

It introduces a data mining model using decision trees to analyze student performance and aid in early intervention in higher education.

Findings

Decision tree classification effectively predicts student performance.

The model helps identify students needing special attention.

Data mining enhances decision-making in educational settings.

Abstract

The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.