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A new ML-based approach to enhance student engagement in online environment
The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s enga...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580220/ https://www.ncbi.nlm.nih.gov/pubmed/34758022 http://dx.doi.org/10.1371/journal.pone.0258788 |
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author | Ayouni, Sarra Hajjej, Fahima Maddeh, Mohamed Al-Otaibi, Shaha |
author_facet | Ayouni, Sarra Hajjej, Fahima Maddeh, Mohamed Al-Otaibi, Shaha |
author_sort | Ayouni, Sarra |
collection | PubMed |
description | The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting. |
format | Online Article Text |
id | pubmed-8580220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85802202021-11-11 A new ML-based approach to enhance student engagement in online environment Ayouni, Sarra Hajjej, Fahima Maddeh, Mohamed Al-Otaibi, Shaha PLoS One Research Article The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting. Public Library of Science 2021-11-10 /pmc/articles/PMC8580220/ /pubmed/34758022 http://dx.doi.org/10.1371/journal.pone.0258788 Text en © 2021 Ayouni et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ayouni, Sarra Hajjej, Fahima Maddeh, Mohamed Al-Otaibi, Shaha A new ML-based approach to enhance student engagement in online environment |
title | A new ML-based approach to enhance student engagement in online environment |
title_full | A new ML-based approach to enhance student engagement in online environment |
title_fullStr | A new ML-based approach to enhance student engagement in online environment |
title_full_unstemmed | A new ML-based approach to enhance student engagement in online environment |
title_short | A new ML-based approach to enhance student engagement in online environment |
title_sort | new ml-based approach to enhance student engagement in online environment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580220/ https://www.ncbi.nlm.nih.gov/pubmed/34758022 http://dx.doi.org/10.1371/journal.pone.0258788 |
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