Classification and prediction based data mining algorithms to predict students' introductory programming performance

Data mining has been successfully implemented in the business world but, its use in higher education is still comparatively new. Predicting students' performance becomes more challenging due to the huge volume of data in educational databases. This paper focus on predicting introductory programming performance of first year bachelor students in Computer Application course by a predictive data mining model using classification based algorithms. The collected data contains the students' demographics, grade in introductory programming at college, and grade in introductory programming at test which contains 60 questions. Collected data was applied on various classification algorithms such as Multilayer Perception, Naïve Bayes, SMO, J48 and REPTree using WEKA. As a result, statistics are generated based on all classification algorithms and comparison of all five classifiers is also done in order to predict the accuracy and to find the best performing classification algorithm among all. In this paper, a knowledge flow model is also drawn for all five classifiers and also this paper showcases the importance of Prediction and Classification based data mining algorithms in the field of programming education and also presents some promising future lines. It could bring the benefits and impacts to students, educators and the academic institutions.