Machine Learning model for student drop-out prediction based on student engagement

Conference paper


Brezočnik, L., Nalli, G., De Leone, R., Val, S., Podgorelec, V. and Karakatič, S. 2023. Machine Learning model for student drop-out prediction based on student engagement. Karabegovic, I., Kovačević, A. and Mandzuka, S. (ed.) 9th International Conference on New Technologies, Development and Application. Sarajevo, Bosnia and Herzegovina 22 - 24 Jun 2023 Cham Springer. pp. 486–496 https://doi.org/10.1007/978-3-031-31066-9_54
TypeConference paper
TitleMachine Learning model for student drop-out prediction based on student engagement
AuthorsBrezočnik, L., Nalli, G., De Leone, R., Val, S., Podgorelec, V. and Karakatič, S.
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.

Sustainable Development Goals4 Quality education
Middlesex University ThemeCreativity, Culture & Enterprise
Conference9th International Conference on New Technologies, Development and Application
Page range486–496
Proceedings TitleNew Technologies, Development and Application VI: Volume 1
SeriesLecture Notes in Networks and Systems
EditorsKarabegovic, I., Kovačević, A. and Mandzuka, S.
ISSN2367-3370
Electronic2367-3389
ISBN
Paperback9783031310652
Electronic9783031310669
PublisherSpringer
Place of publicationCham
Publication dates
Online20 May 2023
Print20 May 2023
Publication process dates
Accepted2023
Deposited14 Jun 2024
Output statusPublished
Accepted author manuscript
File Access Level
Open
Copyright Statement

This version of the paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-ma...), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-31066-9_54

Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-031-31066-9_54
Web address (URL) of conference proceedingshttps://doi.org/10.1007/978-3-031-31066-9
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