A study of feature selection algorithms for predicting students academic performance

• Main Authors: Zaffar, M., Savita, K.S., Hashmani, M.A., Rizvi, S.S.H.
• Format: Article
• Published: Science and Information Organization 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049516304&doi=10.14569%2fIJACSA.2018.090569&partnerID=40&md5=7e4c2d2c412385558d50864f2ddd724a; http://eprints.utp.edu.my/21304/

The main aim of all the educational organizations is to improve the quality of education and elevate the academic performance of students. Educational Data Mining (EDM) is a growing research field which helps academic institutions to improve the performance of their students. The academic institutions are most often judged by the grades achieved by the students in examination. EDM offers different practices to predict the academic performance of students. In EDM, Feature Selection (FS) plays a vital role in improving the quality of prediction models for educational datasets. FS algorithms eliminate unrelated data from the educational repositories and hence increase the performance of classifier accuracy used in different EDM practices to support decision making for educational settings. The good quality of educational dataset can produce better results and hence the decisions based on such quality dataset can increase the quality of education by predicting the performance of students. In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. The results obtained from different FS algorithms and classifiers on two student datasets with different number of features will also help researchers to find the best combinations of filter feature selection algorithms and classifiers. It is very necessary to put light on the relevancy of feature selection for student performance prediction, as the constructive educational strategies can be derived through the relevant set of features. The results of our study depict that there is a 10 difference of prediction accuracies between the results of datasets with different number of features. © 2015 The Science and Information (SAI) Organization Limited.