Genetic algorithm based feature selection for predicting student’s academic performance

• Main Authors: Al Farissi, Al Farissi, Mohamed Dahlan, Halina, Samsuryadi, Samsuryadi
• Format: Conference or Workshop Item
• Published: 2020
• Subjects: HF Commerce
• Online Access: http://eprints.utm.my/id/eprint/92442/; http://dx.doi.org/10.1007/978-3-030-33582-3_11

Recently, student’s academic performance prediction has become an increasingly prominent research topic in the field of Educational Data Mining (EDM). The prediction of student’s academic performance aims to explore information that is beneficial to the learning process of student. Therefore, accurate prediction of student’s academic performance provide benefits for education institutions to improve the quality of their institutions by improving the learning process of students. In predicting the student’s academic performance, the problem of high dimensional dataset is often faced in the datasets which significantly impacts the accuracy of student academic performance prediction. This paper proposed Genetic Algorithm based Feature Selection (GAFS) along with selected single classifier for classification in order to improve the accuracy in predicting student academic performance. Kaggle dataset is used in this paper and two phase of experiment have been conducted, single classifier without GAFS, and single classifier with GAFS. Results from the experiments show that, the accuracy of the proposed GAFS for classification makes an impressive performance in predicting student academic performance in terms of accuracy compare to existing techniques.