Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance

Understanding students’ learning processes and education-related phenomena by extracting knowledge from educational data sets represents a continuous interest in the educational data mining domain. Due to an accelerated expansion of online learning and digitalisation in education, there is a growing...

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Autores principales:	Mariana-Ioana Maier, Gabriela Czibula, Zsuzsanna-Edit Oneţ-Marian
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	unsupervised learning autoencoders t-SNE educational data mining students’ performance analysis online learning Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/1414eabffa594b0dae265d63010edaea
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spelling	oai:doaj.org-article:1414eabffa594b0dae265d63010edaea2021-11-25T18:16:45ZTowards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance10.3390/math92228702227-7390https://doaj.org/article/1414eabffa594b0dae265d63010edaea2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2870https://doaj.org/toc/2227-7390Understanding students’ learning processes and education-related phenomena by extracting knowledge from educational data sets represents a continuous interest in the educational data mining domain. Due to an accelerated expansion of online learning and digitalisation in education, there is a growing interest in understanding the impact of online learning on the academic performance of students. In this study, we comparatively investigate traditional and synchronous online learning methods to assess students’ performance through the use of deep autoencoders. Experiments performed on real data sets collected in both online and traditional learning environments showed that autoencoders are able to detect hidden patterns in academic data sets unsupervised; these patterns are valuable for the prediction of students’ performance. The obtained results emphasized that, for the considered case studies, traditional evaluations are a little more accurate than online evaluations. Still, after applying a one-tailed paired Wilcoxon signed-rank test, no statistically significant difference between the traditional and online evaluations was observed.Mariana-Ioana MaierGabriela CzibulaZsuzsanna-Edit Oneţ-MarianMDPI AGarticleunsupervised learningautoencoderst-SNEeducational data miningstudents’ performance analysisonline learningMathematicsQA1-939ENMathematics, Vol 9, Iss 2870, p 2870 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	unsupervised learning autoencoders t-SNE educational data mining students’ performance analysis online learning Mathematics QA1-939
spellingShingle	unsupervised learning autoencoders t-SNE educational data mining students’ performance analysis online learning Mathematics QA1-939 Mariana-Ioana Maier Gabriela Czibula Zsuzsanna-Edit Oneţ-Marian Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance
description	Understanding students’ learning processes and education-related phenomena by extracting knowledge from educational data sets represents a continuous interest in the educational data mining domain. Due to an accelerated expansion of online learning and digitalisation in education, there is a growing interest in understanding the impact of online learning on the academic performance of students. In this study, we comparatively investigate traditional and synchronous online learning methods to assess students’ performance through the use of deep autoencoders. Experiments performed on real data sets collected in both online and traditional learning environments showed that autoencoders are able to detect hidden patterns in academic data sets unsupervised; these patterns are valuable for the prediction of students’ performance. The obtained results emphasized that, for the considered case studies, traditional evaluations are a little more accurate than online evaluations. Still, after applying a one-tailed paired Wilcoxon signed-rank test, no statistically significant difference between the traditional and online evaluations was observed.
format	article
author	Mariana-Ioana Maier Gabriela Czibula Zsuzsanna-Edit Oneţ-Marian
author_facet	Mariana-Ioana Maier Gabriela Czibula Zsuzsanna-Edit Oneţ-Marian
author_sort	Mariana-Ioana Maier
title	Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance
title_short	Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance
title_full	Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance
title_fullStr	Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance
title_full_unstemmed	Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance
title_sort	towards using unsupervised learning for comparing traditional and synchronous online learning in assessing students’ academic performance
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1414eabffa594b0dae265d63010edaea
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_version_	1718411364158930944

Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance

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