Analyzing Students' Academic Performance through Educational Data Mining

Predicting students' performance is a very important task in any educational system. Therefore, to predict the learner's behavior towards studies many data mining techniques are used like clustering, classification, regression. In this paper, new student's performance prediction model and new features are introduced that have a great influence on student's academic achievement ie student days absence in class and parents' involvement in the learning process. In this paper, considerable attention is on the punctuality of students and the effect of participation of parents in the learning process. This category of features is concerned with the learner's interaction with the e–learning management system. Three different classifiers such as Naive Bayes, Decision Tree, and Artificial Neural Network are used to examine the effect of these features on students' educational performance. The accuracy of the proposed model achieved up to 10% to 15% and is much improved as compared to the results when such features are removed.