SHS Web Conf.
Volume 129, 2021The 21st International Scientific Conference Globalization and its Socio-Economic Consequences 2021
|Number of page(s)
|Economic Sustainability and Economic Resilience
|16 December 2021
Global challenges of students dropout: A prediction model development using machine learning algorithms on higher education datasets
1 University of Pardubice, Faculty of Economics and Administration, Institute of System Engineering and Informatics, Studentská 84, 532 10 Pardubice, Czech Republic
2 University of Pardubice, Faculty of Economics and Administration, Institute of System Engineering and Informatics, Studentská 84, 532 10 Pardubice, Czech Republic
* Corresponding author: email@example.com
Research background: In this era of globalization, data growth in research and educational communities have shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. However, the analysis accuracy is low when the quality of educational data is incomplete. Moreover, the current approaches on dropout prediction cannot utilize available sources.
Purpose of the article: This article aims to develop a prediction model for students’ dropout prediction using machine learning techniques.
Methods: The study used machine learning methods to identify early dropouts of students during their study. The performance of different machine learning methods was evaluated using accuracy, precision, support, and f-score methods. The algorithm that best suits the datasets for these performance measurements was used to create the best prediction model.
Findings & value added: This study contributes to tackling the current global challenges of student dropouts from their study. The developed prediction model allows higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education. It can also help the institutions to plan resources in advance for the coming academic semester and allocate it appropriately. Generally, the learning analytics prediction model would allow higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education.
Key words: Globalization / Prediction / Machine Learning / Learning Analytics / Global challenges
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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