Prediction of students’ performance in online learning using supervised machine learning
International Journal of Information and Learning Technology
ISSN: 2056-4880
Article publication date: 19 April 2024
Issue publication date: 24 April 2024
Abstract
Purpose
This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course.
Design/methodology/approach
The exploration and visualisation of the data were first carried out to gain a better understanding of the students, the course(s) each student was enrolled in and each course’s virtual learning resources. Following this, the construction of the social network graphs was performed to depict how each student behaved online before the degree centralities were computed for each of the nodes in a social network graph. Data pre-processing to assign labels based on the final result a student obtained in a course was then performed before we trained and tested models to predict which students did or did not graduate.
Findings
The study’s findings demonstrate that the constructed predictive model has good performance, as shown by the accuracy, precision, recall and f-measure metrics. The outcomes also showed that students’ use of online resources is a crucial element that influences how well they perform in their academics.
Originality/value
The similarity index is as low as 9%.
Keywords
Citation
Khor, E.T. and Darshan, D. (2024), "Prediction of students’ performance in online learning using supervised machine learning", International Journal of Information and Learning Technology, Vol. 41 No. 2, pp. 166-179. https://doi.org/10.1108/IJILT-08-2023-0148
Publisher
:Emerald Publishing Limited
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