Predicting student performance: a classification model using machine learning algorithms
by Esra'a Alshdaifat; Aisha Zaid; Ala'a Alshdaifat
International Journal of Business Information Systems (IJBIS), Vol. 39, No. 3, 2022

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.

Online publication date: Thu, 21-Apr-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 Information Systems (IJBIS):
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