Predicting students' academic performance using machine learning techniques: a literature review. (1st June 2022)

• Record Type: Journal Article
• Authors: Nabil, Aya; Seyam, Mohammed; Abou-Elfetouh, Ahmed
• Is Part Of: International journal of business intelligence and data mining. Volume 20:Number 4(2022)
• Page Start: 456
• Page End: 479
• Publication Date: 2022-06-01
• Subjects: data mining -- educational data mining -- EDM -- prediction -- student academic performance -- machine learning techniques -- deep learning; 006.312
• Languages: English
• ISSNs: 1743-8187

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.