2026-04-17T09:35:42Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73452023-03-21T13:16:59Zcom_10259_5841com_10259_5086com_10259_2604com_10259_4219col_10259_5842col_10259_4220

00925njm 22002777a 4500 dc Sáiz Manzanares, María Consuelo author Marticorena Sánchez, Raúl author Ochoa Orihuel, Javier author 2021-10 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. http://hdl.handle.net/10259/7345 10.3390/electronics10212620 2079-9292 Advanced learning technologies LMS Machine learning Self-regulated learning Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques