Students' Academic Performance Prediction using Data Mining

Educational Data Mining has been an emerging topic nowadays due to the growth of educational data. This field makes it possible to develop methods in order to find out hidden patterns from educational data. The methods extracted from Educational Data Mining discipline are then used to understand students including their learning behavior as well as to predict their academic performance. This study proposes a model for predicting the academic performance of Computer Science students using Data Mining technique. The data were collected using questionnaires that contain the students' demographics, previous GPA, and family background information. Two Data Mining models (Decision Tree and Random Forest) are applied to the students' data to create the best student's academic performance prediction model. The result of this study shows that Decision Tree is the best model compared to Random Forest by receiving the highest accuracy value of 66.9%. This study shows that there are some relevant feature that influenced student's academic performance.