Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this researc...

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Autores principales:	Patricio Ramírez-Correa, Jorge Alfaro-Pérez, Mauricio Gallardo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	learning styles machine learning hybrid university teaching Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/9350b4dff25e4debb1feab104630de0b
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id	oai:doaj.org-article:9350b4dff25e4debb1feab104630de0b
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spelling	oai:doaj.org-article:9350b4dff25e4debb1feab104630de0b2021-11-25T16:29:57ZIdentifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques10.3390/app1122105052076-3417https://doaj.org/article/9350b4dff25e4debb1feab104630de0b2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10505https://doaj.org/toc/2076-3417In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%; this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment.Patricio Ramírez-CorreaJorge Alfaro-PérezMauricio GallardoMDPI AGarticlelearning stylesmachine learninghybrid university teachingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10505, p 10505 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	learning styles machine learning hybrid university teaching Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	learning styles machine learning hybrid university teaching Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Patricio Ramírez-Correa Jorge Alfaro-Pérez Mauricio Gallardo Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
description	In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%; this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment.
format	article
author	Patricio Ramírez-Correa Jorge Alfaro-Pérez Mauricio Gallardo
author_facet	Patricio Ramírez-Correa Jorge Alfaro-Pérez Mauricio Gallardo
author_sort	Patricio Ramírez-Correa
title	Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
title_short	Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
title_full	Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
title_fullStr	Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
title_full_unstemmed	Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
title_sort	identifying engineering undergraduates’ learning style profiles using machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9350b4dff25e4debb1feab104630de0b
work_keys_str_mv	AT patricioramirezcorrea identifyingengineeringundergraduateslearningstyleprofilesusingmachinelearningtechniques AT jorgealfaroperez identifyingengineeringundergraduateslearningstyleprofilesusingmachinelearningtechniques AT mauriciogallardo identifyingengineeringundergraduateslearningstyleprofilesusingmachinelearningtechniques
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Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

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