AN EMPIRICAL STUDY OF MACHINE LEARNING ALGORITHMS TO PREDICT STUDENTS’ GRADES

Abstract

Machine learning algorithms provide an opportunity to analyze the existing educational data and predict futuristic needs. In present study a model was proposed to classify students’ grades by employing machine learning algorithms. The important parameters from profiles and preferences were considered for classification purpose. Five classification algorithms i.e. Decision Table, OneR, J48, Random Forest and Random Tree were used for model construction and prediction of grades. The pattern analysis was done by WEKA open source data mining tool. The J48 was found to be the best algorithm in predicting grades with highest accuracy of 78 %. The accuracies obtained from Random Forest, Random Tree, Decision Table and OneR were 73, 72, 58.28 and 39.9 % respectively, which were lower than the accuracy obtained by J48. The results showed the effectiveness of machine learning algorithms to predict the performance of students.