Mining Educational Data to Analyze Students Performance

TLDR

Higher education institutions aim to deliver quality education, which can be enhanced by uncovering predictive knowledge from educational data—such as enrollment patterns, teaching model alienation, cheating detection, abnormal results, and performance forecasts—using data mining techniques. This study presents a data mining model to demonstrate the effectiveness of data mining techniques for higher education at a university. The authors employ a decision‑tree classification approach to assess student performance. The decision‑tree model extracts end‑semester performance patterns, enabling early identification of dropouts and students requiring targeted advising.

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.