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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
Hew, Khe Foon · Qiao, Chen · Tang, Ying

PublishedJuly 2018
JournalThe International Review of Research in Open and Distributed Learning
Volume 19, Issue 3, Pages 70-93
PublisherAthabasca, University
CountryHong Kong, Asia

ABSTRACT
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.

Keywords engagement · machine learning · massive open online courses · MOOCs · text mining

Published atAthabasca, AB
ISSN1492-3831
RefereedYes
RightsAttribution 4.0 International (CC BY 4.0)
DOI10.19173/irrodl.v19i3.3596
URLhttp://www.irrodl.org/index.php/irrodl/article/view/3596
Export optionsBibTex · EndNote · Tagged XML · Google Scholar



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