Abstract
Nowadays, the issue of student drop-out is addressed not only through the prism of pedagogy, but also by technological practices. In this paper, we demonstrate how a student drop-out could be predicted through a student’s performance using different Machine Learning techniques, i.e., supervised learning and unsupervised learning. The results show that various types of student engagement are essential factors in predicting drop-out and the final ECTS points achievements.
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Acknowledgements
The authors acknowledge the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057), and the European Commission (Project Code 2020-1-ES01-KA203-082090).
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Brezočnik, L., Nalli, G., De Leone, R., Val, S., Podgorelec, V., Karakatič, S. (2023). Machine Learning Model for Student Drop-Out Prediction Based on Student Engagement. In: Karabegovic, I., Kovačević, A., Mandzuka, S. (eds) New Technologies, Development and Application VI. NT 2023. Lecture Notes in Networks and Systems, vol 687. Springer, Cham. https://doi.org/10.1007/978-3-031-31066-9_54
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DOI: https://doi.org/10.1007/978-3-031-31066-9_54
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