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A data mining approach using machine learning algorithms for early detection of low-performing students

Ean Teng Khor (National Institute of Education, Nanyang Technological University, Singapore, Singapore)

International Journal of Information and Learning Technology

ISSN: 2056-4880

Article publication date: 24 February 2022

Issue publication date: 12 April 2022

228

Abstract

Purpose

The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance.

Design/methodology/approach

For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then followed by data pre-processing and feature engineering (Step 2). Next, the author conducted data modelling and prediction (Step 3). Finally, the performance of the developed models was evaluated (Step 4).

Findings

The paper found that the decision trees algorithm outperformed other machine learning algorithms. The study also confirms the significant effect of the academic background and virtual learning environment (VLE) interactions feature categories to academic performance. The accuracy enhancement is 17.66% for decision trees classifier, 3.49% for logistic regression classifier and 4.89% for neural networks classifier. Based on the results of CorrelationAttributeEval technique with the use of a ranker search method, the author found that the assessment_score and sum_click features are more important among academic background and VLE interactions feature categories for the classification analysis in predicting students' academic performance.

Originality/value

The work meets the originality requirement.

Keywords

Citation

Khor, E.T. (2022), "A data mining approach using machine learning algorithms for early detection of low-performing students", International Journal of Information and Learning Technology, Vol. 39 No. 2, pp. 122-132. https://doi.org/10.1108/IJILT-09-2021-0144

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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