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...

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Autores principales:	Sarra Ayouni, Fahima Hajjej, Mohamed Maddeh, Shaha Al-Otaibi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/05801f55521544b2b32cfb61defdb5f5
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spelling	oai:doaj.org-article:05801f55521544b2b32cfb61defdb5f52021-11-18T07:37:11ZA new ML-based approach to enhance student engagement in online environment1932-6203https://doaj.org/article/05801f55521544b2b32cfb61defdb5f52021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580220/?tool=EBIhttps://doaj.org/toc/1932-6203The 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.Sarra AyouniFahima HajjejMohamed MaddehShaha Al-OtaibiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sarra Ayouni Fahima Hajjej Mohamed Maddeh Shaha Al-Otaibi A new ML-based approach to enhance student engagement in online environment
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	article
author	Sarra Ayouni Fahima Hajjej Mohamed Maddeh Shaha Al-Otaibi
author_facet	Sarra Ayouni Fahima Hajjej Mohamed Maddeh Shaha Al-Otaibi
author_sort	Sarra Ayouni
title	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_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_sort	new ml-based approach to enhance student engagement in online environment
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/05801f55521544b2b32cfb61defdb5f5
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A new ML-based approach to enhance student engagement in online environment

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