부산대학교 도서관

These days, educational data mining is a new research area that is being used for data exploration in educational settings for the prediction of student’s performance. In online educational system, the behavioral features of learners play an important role for judging the interaction between students and the Learning Management System. Here, in this article, new features known as behavioral features of students is used and performance is evaluated using classifiers such as Support Vector Machine, K-Nearest Neighbor and Decision Tree. Moreover, in order to enhance the classifier’s performance, the collaborative methods such as Bagging, Boosting and Random Forest are used. Accuracy of 87.4% was archived when the collaborative techniques were applied to the classifiers for improving the performance in academics.