EXPLORING THE PREDICTIVE POTENTIAL OF EDUCATIONAL DATA MINING: A STUDY ON UNIVERSITY STUDENTS' ACADEMIC PERFORMANCE

Abstract

Predicting student’s performance become more tough due to a huge volume of data in educational database. Even though data mining has been efficiently implemented in the business world. Use of data mining is relatively new in higher education, i.e., it used for recognition and extraction of potentially and advanced precious knowledge from huge amount of data using the concept of data mining. Model should be developed which can determine the conclusion from student’s academic success. In this study,the concept of educational data mining have been used. It is a computational process of discovering patterns in a large dataset and transform it into understandable structure for further use. Student data collected from different institutes. The scope of this research is to identify the factors influencing the performance of students in universities. To predict the performance of university student, for clustering purposes K mean algorithm and two algorithms of classification Decision tree (J48) and Naive Bays have been used,also compare the results of Decision tree J48 and Naive Bay’s algorithms which gives the better results according to their accuracy . For this purpose, weka tool was used to implement the algorithms. This study helps the educational institutions to identify such factors which affect the student performance and to eliminate these causes to enhance their performance.