RnkHEU: A Hybrid Feature Selection Method for Predicting Students' Performance

Abstract

Predicting students' performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students' performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students' performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students' performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students' performance with commonly used classifiers about 10% and improve the precision of predicting students' academic failure by up to 45%.