Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs

Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze...

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Autores principales:	Khe Foon Hew, Chen Qiao, Ying Tang
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2018
Materias:	MOOCs massive open online courses engagement text mining machine learning Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/9bc77b0dde7f4732be75a6a3c967a0e6
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spelling	oai:doaj.org-article:9bc77b0dde7f4732be75a6a3c967a0e62021-12-02T19:20:53ZUnderstanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs10.19173/irrodl.v19i3.35961492-3831https://doaj.org/article/9bc77b0dde7f4732be75a6a3c967a0e62018-07-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/3596https://doaj.org/toc/1492-3831Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses. Khe Foon HewChen QiaoYing TangAthabasca University PressarticleMOOCsmassive open online coursesengagementtext miningmachine learningSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 19, Iss 3 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	MOOCs massive open online courses engagement text mining machine learning Special aspects of education LC8-6691
spellingShingle	MOOCs massive open online courses engagement text mining machine learning Special aspects of education LC8-6691 Khe Foon Hew Chen Qiao Ying Tang Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs
description	Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses.
format	article
author	Khe Foon Hew Chen Qiao Ying Tang
author_facet	Khe Foon Hew Chen Qiao Ying Tang
author_sort	Khe Foon Hew
title	Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs
title_short	Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs
title_full	Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs
title_fullStr	Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs
title_full_unstemmed	Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs
title_sort	understanding student engagement in large-scale open online courses: a machine learning facilitated analysis of student’s reflections in 18 highly rated moocs
publisher	Athabasca University Press
publishDate	2018
url	https://doaj.org/article/9bc77b0dde7f4732be75a6a3c967a0e6
work_keys_str_mv	AT khefoonhew understandingstudentengagementinlargescaleopenonlinecoursesamachinelearningfacilitatedanalysisofstudentsreflectionsin18highlyratedmoocs AT chenqiao understandingstudentengagementinlargescaleopenonlinecoursesamachinelearningfacilitatedanalysisofstudentsreflectionsin18highlyratedmoocs AT yingtang understandingstudentengagementinlargescaleopenonlinecoursesamachinelearningfacilitatedanalysisofstudentsreflectionsin18highlyratedmoocs
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Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs

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