Improving Student Academic Performance Prediction Models using Feature Selection

This paper presents methods to improve the prediction of student academic performance using feature selection by removing misclassified instances and Synthetic Minority Over-Sampling Technique. It compares the performance of seven students' academic performance prediction models, namely Naïve Bayes, Sequential Minimum Optimization, Artificial Neural Network, k-Nearest Neighbor, REPTree, Partial decision trees, and Random Forest. The data were collected from 9,458 students at the Rajabhat Maha Sarakham University, Thailand during 2015 - 2018. The model performances were evaluated with precision, recall, and F-measure. The experimental results indicated that the Random Forest approach significantly improves the performance of students' academic performance prediction models with precision up to 41.70%, recall up to 41.40% and F-measure up to 41.60%, respectively.