THE MATRICULATION STUDENTS’ RESULT PREDICTION SYSTEM (RAKHINE STATE)

Abstract

Nowadays, large amount of data are stored in different databases and are increasing rapidly. These databases contain data that can be useful for predicting students’ academic performance, and will help to improve the academic educational environment. Educational Data Mining is used to study the available data stored in the system database and to create new knowledge out of it. C4.5 (J48) classification algorithm is applied to create a decision tree model that will predict the academic performance of Information technology students of the Rakhine State. The result of the decision tree predicts the possible students who will have the chance to graduate or do not have the chance to graduate based on their historic data, and this will help teachers to provide appropriate inputs to help the failing students.