Title: Predicting students' academic performance using machine learning techniques: a literature review

Authors: Aya Nabil; Mohammed Seyam; Ahmed Abou-Elfetouh

Addresses: Department of Information Systems, Faculty of Computers and Information, Mansoura University, Egypt ' Department of Information Systems, Faculty of Computers and Information, Mansoura University, Egypt ' Department of Information Systems, Faculty of Computers and Information, Mansoura University, Egypt

Abstract: The amount of students' data stored in educational databases is increasing rapidly. These databases contain hidden patterns and useful information about students' behaviour and performance. Data mining is the most effective method to analyse the stored educational data. Educational data mining (EDM) is the process of applying different data mining techniques in educational environments to analyse huge amounts of educational data. Several researchers applied different machine learning techniques to analyse students' data and extract hidden knowledge from them. Prediction of students' academic performance is necessary for educational environments to measure the quality of the learning process. Therefore, it is one of the most common applications of EDM. In this survey paper, we present a review of data mining techniques, EDM and its applications, and discuss previous studies in predicting students' academic performance. An analysis of different machine learning techniques used in previous studies is also presented in this paper.

Keywords: data mining; educational data mining; EDM; prediction; student academic performance; machine learning techniques; deep learning.

DOI: 10.1504/IJBIDM.2022.123214

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.4, pp.456 - 479

Received: 23 May 2020
Accepted: 01 Nov 2020
Published online: 03 Jun 2022 *

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