Title: A review on feature selection methods for improving the performance of classification in educational data mining

Authors: Maryam Zaffar; Manzoor Ahmed Hashmani; K.S. Savita; Sameer Ahmad Khan

Addresses: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Malaysia ' High Performance Cloud Computing Center, Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Malaysia ' Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Malaysia ' Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Malaysia

Abstract: Educational data mining (EDM) evaluates and predicts students' performance that assists to discover important factors affecting students' academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying feature selection algorithms. Feature selection algorithms help in selecting robots and meaningful features for predicting students' performance with high accuracy. This paper presents different EDM approaches for forecasting students' performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in educational data mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.

Keywords: educational data mining; EDM; classification algorithms; feature selection in educational data mining; filter feature selection; wrapper feature selection.

DOI: 10.1504/IJITM.2021.114161

International Journal of Information Technology and Management, 2021 Vol.20 No.1/2, pp.110 - 131

Received: 28 Apr 2017
Accepted: 17 Oct 2017
Published online: 12 Apr 2021 *

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