Predicting students' academic performance using machine learning techniques: a literature review
by Aya Nabil; Mohammed Seyam; Ahmed Abou-Elfetouh
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 20, No. 4, 2022

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.

Online publication date: Fri, 03-Jun-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com