Enhancing adaptive learning with Moodle's machine learning

Liliia O. Fadieieva

doi:10.31812/ed.625

Authors

Liliia O. Fadieieva Kryvyi Rih State Pedagogical University https://orcid.org/0000-0003-4906-2539

DOI:

https://doi.org/10.31812/ed.625

Keywords:

Moodle, machine learning, adaptive learning, personalized education, learning analytics, education technology

Abstract

This review explores how Moodle's machine learning capabilities enhance adaptive learning. We analyze five studies using Moodle for predictive and prescriptive support in education. These studies cover topics like learner classification, early risk detection, predictor comparison, reliability analysis, and custom indicators. We extract key findings, and address challenges while suggesting future research directions. This review offers insights for educators and researchers aiming to personalize education with Moodle.

Downloads

Download data is not yet available.

Abstract views: 312 / PDF views: 117

References

Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions (2020), URL https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020SC0209&qid=1647943853396

Digital Education Action Plan (2021–2027) (2020), URL https://education.ec.europa.eu/focus-topics/digital-education/digital-education-action-plan

Bognár, L., Fauszt, T.: Different learning predictors and their effects for Moodle Machine Learning models. In: 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 000405–000410 (2020), doi:10.1109/CogInfoCom50765.2020.9237894 DOI: https://doi.org/10.1109/CogInfoCom50765.2020.9237894

Bognár, L., Fauszt, T., Nagy, G.Z.: Analysis of Conditions for Reliable Predictions by Moodle Machine Learning Models. International Journal of Emerging Technologies in Learning (iJET) 16(06), 106–121 (Mar 2021), doi:10.3991/ijet.v16i06.18347 DOI: https://doi.org/10.3991/ijet.v16i06.18347

Cechinel, C., De Freitas Dos Santos, M., Barrozo, C., Schardosim, J.E., Vila, E.d., Ramos, V., Primo, T., Munoz, R., Queiroga, E.M.: A Learning Analytics Dashboard for Moodle: Implementing Machine Learning Techniques to Early Detect Students at Risk of Failure. In: 2021 XVI Latin American Conference on Learning Technologies (LACLO), pp. 130–136 (2021), doi:10.1109/LACLO54177.2021.00019 DOI: https://doi.org/10.1109/LACLO54177.2021.00019

Fauszt, T., Bognár, L., Sándor, Á.: Increasing the prediction power of moodle machine learning models with self-defined indicators. International Journal of Emerging Technologies in Learning (iJET) 16(24), 23–39 (Dec 2021), doi:10.3991/ijet.v16i24.23923 DOI: https://doi.org/10.3991/ijet.v16i24.23923

Hassan, S., El Fattah Hegazy, A.: A model recommends best machine learning algorithm to classify learners based on their interactivity with Moodle. In: 2015 Second International Conference on Computing Technology and Information Management (ICCTIM), pp. 49–54 (2015), doi:10.1109/ICCTIM.2015.7224592 DOI: https://doi.org/10.1109/ICCTIM.2015.7224592

Ministry of Education and Science of Ukraine: Nakaz pro zatverdzhennia Kontseptsii rozvytku pedahohichnoi osvity [Order on approval of the Concept of pedagogical education development] (2018), URL http://tinyurl.com/4ap3z938

Verkhovna Rada of Ukraine: Law of Ukraine On Higher Education (2014), URL https://zakon.rada.gov.ua/laws/show/1556-18

Verkhovna Rada of Ukraine: Law of Ukraine On Education (2018), URL https://zakon.rada.gov.ua/laws/show/2145-19