Using Decision Tree Classifier for Analyzing Students’ Activities

Abstract

In this paper students’ activities data analysis in the course Introduction to programming at Faculty of Electrical Engineering in East Sarajevo is performed. Using the data that are stored in the Moodle database combined with manually collected data, the model was developed to predict students’ performance in successfully passing the final exam. The goal was to identify variables that could help teachers in predicting students’ performance and making specific recommendations for improving individual activities that could directly influence final exam successful passing. The model was created using decision tree classifier and experiments were performed using the WEKA data mining tool. The effect of input attributes on the model performances was analyzed and applying appropriate techniques a higher accuracy of the generated model was achieved.