Behavioral predictors of MOOC post-course development

Yuan Wang, Ryan S. Baker, Luc Paquette

Research output: Contribution to journalConference article

Abstract

Massive Online Open Courses (MOOCs) have shown potential for promoting learning at scale. A plethora of studies have tapped into in-course learner behaviors to predict learner success. Yet few studies have looked to the relation between performance and engagement during the course and career development after the course. As such, the present study collected and analyzed both in-course data reflecting learner achievement and engagement in a postgraduate-level MOOC, as well as post-course career development. The goal of this research is to examine how career advancers differ from the rest of learners in terms of their performance and engagement within the course. Results showed that career advancers earned better scores and were more likely to complete the course. Career advancers also engaged more frequently with all key course components such as course pages, lecture videos, assignment submissions, and discussion forums. However, while they read the forums, they were not significantly more likely to post, comment, or vote.

Original languageEnglish (US)
Pages (from-to)100-111
Number of pages12
JournalCEUR Workshop Proceedings
Volume1967
StatePublished - Jan 1 2017
EventJoint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Keywords

  • Career development
  • Learning analytics
  • Learning outcomes
  • Long-term learning development
  • Massive online open courses

ASJC Scopus subject areas

  • Computer Science(all)

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