Case Study of UPNM Students Performance Classification Algorithms

Abstract

Most students have a problem to keep track on their learning performance. Some lecturers with high teaching hours and burden of administration jobs may have difficulty to identify weak and low performance students. In this study, three classification techniques are applied on educational datasets to predict the students’ performance based on coursework assessments. Thus, this prediction results may help lecturers and students to improve their teaching and learning process. The objective of study is to predict students’ performance based on coursework assessments using classification algorithms. The selected classification algorithms applied in this study such as J48 Decision Tree, Naïve Bayes and kNN. WEKA is used as an experimental tool. The selected algorithms are applied on a data of student database of Data Mining subject. Findings shows Naïve Bayes outperforms other classification algorithms with above 80% prediction rate. Thus, the students’ performance for Data Mining Subject is improved. As a conclusion, the classification algorithms can predict students’ performance on a particular subject based on coursework assessments. Â