Case Study of UPNM Students Performance Classification Algorithms - B. Rahayu, Syarifah; D. Kamarudin, Nur; Zainol, Zuraini (International Journal of Engineering and Technology, 2018)
Most students have a problem to keep track on their learning performance.Â 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 studentsâ€™ 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 studentsâ€™ performance based on coursework assessments using classification algorithms. The selected classification algorithms applied in this study such as J48 Decision Tree, NaÃ¯ve 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 NaÃ¯ve Bayes outperforms other classification algorithms with above 80% prediction rate. Thus, the studentsâ€™ performance for Data Mining Subject is improved. As a conclusion, the classification algorithms can predict studentsâ€™ performance on a particular subject based on coursework assessments.Â Â