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Early Identification of At-Risk Students in Online Education: A Deep Learning Approach to Predictive Modelling Cover

Early Identification of At-Risk Students in Online Education: A Deep Learning Approach to Predictive Modelling

Business Systems Research Journal
Volume 16 (2025): Issue 2 (December 2025)
By: ,   and    
Open Access
|Dec 2025
Authors

Authors

Lediana Shala Riza

ls31402@seeu.edu.mk

South East European University, Faculty of Contemporary Sciences and Technologies, North Macedonia

Lejla Abazi Bexheti

l.abazi@seeu.edu.mk

South East European University, Faculty of Contemporary Sciences and Technologies, North Macedonia

Jovana Zoroja

jzoroja@efzg.hr

Faculty of Economics and Business, University of Zagreb, Croatia
DOI: https://doi.org/10.2478/bsrj-2025-0019 | Journal eISSN: 1847-9375 | Journal ISSN: 1847-8344
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Language: English
Page range: 69 - 91
Submitted on: Jan 14, 2025
Accepted on: Aug 15, 2025
Published on: Dec 21, 2025
Published by: IRENET - Society for Advancing Innovation and Research in Economy
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
student performance prediction,
online learning environments,
LSTM,
deep learning framework,
SMOTE
Related subjects:
Business and economics,
Business management,
Management, organization, corporate governance,
Business management, other,
Mathematics and statistics for economists,
Mathematics

© 2025 Lediana Shala Riza, Lejla Abazi Bexheti, Jovana Zoroja, published by IRENET - Society for Advancing Innovation and Research in Economy
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 16 (2025): Issue 2 (December 2025)
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