2026-04-17T10:25:37Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/73452023-03-21T13:16:59Zcom_10259_5841com_10259_5086com_10259_2604com_10259_4219col_10259_5842col_10259_4220
Repositorio Institucional de la Universidad de Burgos
author
Sáiz Manzanares, María Consuelo
author
Marticorena Sánchez, Raúl
author
Ochoa Orihuel, Javier
2023-01-31T08:30:28Z
2023-01-31T08:30:28Z
2021-10
http://hdl.handle.net/10259/7345
10.3390/electronics10212620
2079-9292
The use of advanced learning technologies (ALT) techniques in learning management
systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized
monitoring of their students throughout the teaching–learning process. However, the application of
educational data mining (EDM) techniques, such as supervised and unsupervised machine learning,
is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to
determine which of the ALT resources would be the best predictor and the best classifier of learning
outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether
the groupings found in the clusters coincide with the students’ group of origin. We worked with
a sample of third-year students completing Health Sciences degrees. The results indicate that the
combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and
student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally,
the degree of relationship between the source group and the found cluster was medium (C = 0.61). It
is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to
facilitate their use by teachers.
eng
Atribución 4.0 Internacional
Advanced learning technologies
LMS
Machine learning
Self-regulated learning
Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
info:eu-repo/semantics/article
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URL
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Saiz-electronics_2021.pdf