Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject
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Resumo
This work addresses the problem of inferring student performance from information acquired in a Learning Management System (LMS). In particular, we explore the capabilities offered by Moodle, a widely used LMS. The study is performed on data acquired from four classes within the same subject, for whom we make inferences about student performance marks. The developed methodology describes the degree in which the acquired information allows to predict the student marks belonging to the continuous evaluation, while the prediction of the final students marks has a higher intrinsic complexity that would require a more in- depth study of the relevant variables. Then, we follow a fully data-driven process to discover similarities among classes. In particular, we propose the use of a dependence estimation measure, the normalized Hilbert-Schmidt Independence criterion. We show how this dependence measure is useful to determine relations among classes, based on their particular teaching methodology, by only using data acquired from the LMS. This opens the door to explore the capabilities of LMS in similarity search between offered courses along an educational platform. In order to help the community and serve as a common way of comparison, we provide the source code of the proposed methodology.
Dados da obra
- Tipo
- Artigo
- Periódico
- IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, v. 18, n. 1, p. 19-27
- Editora
- Institute of Electrical and Electronics Engineers
- Ano
- 2023 (2023-02-01)
- Idioma
- en
- Palavras-chave
- Moodle; virtual learning environments; student performance prediction; mathematics education; dependence estimation; course comparison; educational data mining
- DOI
- 10.1109/rita.2023.3250434
- ISSN
- 1932-8540, 2374-0132
- Licença
- cc-by-nc
- Acesso
- Acesso aberto
- Fontes
- OpenAlex
- Na Biblioteca desde
- 01/10/2026 · verificada em 03/10/2026
Como citar
ABNT
PÉREZ-SUAY, Adrián et al.. Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, v. 18, n. 1, p. 19-27, 2023. DOI: https://doi.org/10.1109/rita.2023.3250434.
APA
Pérez-Suay, A., Vaerenbergh, S. V., Diago, P. D., Pascual-Venteo, A. B., & Ferri, F. J. (2023). Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 18(1), 19-27. https://doi.org/10.1109/rita.2023.3250434
BibTeX
@article{perezsuay2023,
author = {Pérez-Suay, Adrián and Vaerenbergh, Steven Van and Diago, Pascual D. and Pascual-Venteo, Ana B. and Ferri, Francesc J.},
title = {Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject},
year = {2023},
journal = {IEEE Revista Iberoamericana de Tecnologias del Aprendizaje},
volume = {18},
number = {1},
pages = {19-27},
publisher = {Institute of Electrical and Electronics Engineers},
doi = {10.1109/rita.2023.3250434},
language = {en}
}RIS
TY - JOUR AU - Pérez-Suay, Adrián AU - Vaerenbergh, Steven Van AU - Diago, Pascual D. AU - Pascual-Venteo, Ana B. AU - Ferri, Francesc J. TI - Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject PY - 2023 JO - IEEE Revista Iberoamericana de Tecnologias del Aprendizaje VL - 18 IS - 1 SP - 19 EP - 27 DO - 10.1109/rita.2023.3250434 LA - en ER -
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