Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

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 min...

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Autores principales:	María Consuelo Sáiz-Manzanares, Raúl Marticorena-Sánchez, Javier Ochoa-Orihuel
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	advanced learning technologies LMS machine learning self-regulated learning Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/02865f29747b453fae16951904d8e162
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id	oai:doaj.org-article:02865f29747b453fae16951904d8e162
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spelling	oai:doaj.org-article:02865f29747b453fae16951904d8e1622021-11-11T15:38:24ZUsing Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques10.3390/electronics102126202079-9292https://doaj.org/article/02865f29747b453fae16951904d8e1622021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2620https://doaj.org/toc/2079-9292The 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.María Consuelo Sáiz-ManzanaresRaúl Marticorena-SánchezJavier Ochoa-OrihuelMDPI AGarticleadvanced learning technologiesLMSmachine learningself-regulated learningElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2620, p 2620 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	advanced learning technologies LMS machine learning self-regulated learning Electronics TK7800-8360
spellingShingle	advanced learning technologies LMS machine learning self-regulated learning Electronics TK7800-8360 María Consuelo Sáiz-Manzanares Raúl Marticorena-Sánchez Javier Ochoa-Orihuel Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
description	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.
format	article
author	María Consuelo Sáiz-Manzanares Raúl Marticorena-Sánchez Javier Ochoa-Orihuel
author_facet	María Consuelo Sáiz-Manzanares Raúl Marticorena-Sánchez Javier Ochoa-Orihuel
author_sort	María Consuelo Sáiz-Manzanares
title	Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_short	Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_full	Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_fullStr	Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_full_unstemmed	Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques
title_sort	using advanced learning technologies with university students: an analysis with machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/02865f29747b453fae16951904d8e162
work_keys_str_mv	AT mariaconsuelosaizmanzanares usingadvancedlearningtechnologieswithuniversitystudentsananalysiswithmachinelearningtechniques AT raulmarticorenasanchez usingadvancedlearningtechnologieswithuniversitystudentsananalysiswithmachinelearningtechniques AT javierochoaorihuel usingadvancedlearningtechnologieswithuniversitystudentsananalysiswithmachinelearningtechniques
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Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

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