RT info:eu-repo/semantics/article
T1 Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
A1 Sáiz Manzanares, María Consuelo
A1 Marticorena Sánchez, Raúl
A1 Ochoa Orihuel, Javier
K1 Advanced learning technologies
K1 LMS
K1 Machine learning
K1 Self-regulated learning
K1 Informática
K1 Computer science
K1 Psicología
K1 Psychology
AB The use of advanced learning technologies (ALT) techniques in learning managementsystems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalizedmonitoring of their students throughout the teaching–learning process. However, the application ofeducational 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) todetermine which of the ALT resources would be the best predictor and the best classifier of learningoutcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whetherthe groupings found in the clusters coincide with the students’ group of origin. We worked witha sample of third-year students completing Health Sciences degrees. The results indicate that thecombination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, andstudent 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). Itis necessary to include ALT resources and the greater automation of EDM techniques in the LMS tofacilitate their use by teachers.
PB MDPI
YR 2021
FD 2021-10
LK http://hdl.handle.net/10259/7345
UL http://hdl.handle.net/10259/7345
LA eng
NO This research was funded by the MINISTERIO DE CIENCIA E INNOVACIÓN, grant number PID2020-117111RB-I00.
DS Repositorio Institucional de la Universidad de Burgos
RD 30-abr-2024