Title: Predicting student performance: a classification model using machine learning algorithms

Authors: Esra'a Alshdaifat; Aisha Zaid; Ala'a Alshdaifat

Addresses: Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' Department of Software Engineering, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' Department of Computer Information System, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan

Abstract: With the increasing availability of educational databases, extraction of interesting patterns and relationships from such data becomes extremely attractive and challenging. Discovering implicit patterns related to student performance is potentially helpful to enhance student achievement. In this paper, a student performance prediction model is generated utilising machine learning algorithms. The central idea is that identifying the dominant features that affect student performance results in generating an effective student performance prediction model. In order to achieve this goal different feature selection approaches are considered. The reported experimental results indicated that the effectiveness of student performance prediction model is significantly affected by the dimensions featured in the considered dataset.

Keywords: student performance; classification; grade prediction; educational data; pattern extraction; feature selection.

DOI: 10.1504/IJBIS.2020.10025386

International Journal of Business Information Systems, 2022 Vol.39 No.3, pp.349 - 364

Received: 26 Mar 2019
Accepted: 06 Jul 2019

Published online: 21 Apr 2022 *

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