Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks
An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learnin...
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doaj-2d5ee2f09c66464e952cff473b34ffc32021-10-01T23:01:16ZengIEEEIEEE Access2169-35362021-01-01913259213260810.1109/ACCESS.2021.31150249546766Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural NetworksSoledad Delgado0https://orcid.org/0000-0003-4868-3712Federico Moran1Jose Carlos San Jose2https://orcid.org/0000-0001-6052-5034Daniel Burgos3https://orcid.org/0000-0003-0498-1101Departamento de Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, SpainDepartamento de Bioquímica y Biología Molecular, Universidad Complutense de Madrid, Madrid, SpainResearch Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, SpainResearch Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, SpainAn accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categories. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts.https://ieeexplore.ieee.org/document/9546766/Artificial neural networksdata science applications in educationdistance education and online learningpattern analysisself-organizing map (SOM)student behaviour |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Soledad Delgado Federico Moran Jose Carlos San Jose Daniel Burgos |
spellingShingle |
Soledad Delgado Federico Moran Jose Carlos San Jose Daniel Burgos Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks IEEE Access Artificial neural networks data science applications in education distance education and online learning pattern analysis self-organizing map (SOM) student behaviour |
author_facet |
Soledad Delgado Federico Moran Jose Carlos San Jose Daniel Burgos |
author_sort |
Soledad Delgado |
title |
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks |
title_short |
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks |
title_full |
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks |
title_fullStr |
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks |
title_full_unstemmed |
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks |
title_sort |
analysis of students’ behavior through user clustering in online learning settings, based on self organizing maps neural networks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categories. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts. |
topic |
Artificial neural networks data science applications in education distance education and online learning pattern analysis self-organizing map (SOM) student behaviour |
url |
https://ieeexplore.ieee.org/document/9546766/ |
work_keys_str_mv |
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