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Friday, December 6 • 11:30am - 12:00am
MOOC Learner Motivation and Course Completion Rates (30m)

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The landscape of online learning has evolved in a synchronous fashion with the development of the every-growing repertoire of technologies, especially with the recent addition of Massive Online Open Courses (MOOCs). Numerous past circumscriptions associated with learning and teaching bounded by time and space dissolved accordingly, so does the parameters of the definition of “online learners”.
The proposed research here is a preliminary attempt to study how different motivational aspects of MOOC learners correlate with course completion rates by conducting a pre-course survey. The primary objective of this proposed project is to explore how various motivational aspects of MOOC learners corresponds with degrees of course completion. In the long run, research results expect to inform future interventions, research and design of MOOCs and reflecting potential emergent needs of MOOC learners.
Given the heterogeneity of motivational aspects of MOOC learners and the observed prevalence of emphasis on course completion rate, this proposed research intends to expand our understanding of MOOC learners by analyzing how MOOC learners’ motivation correlates with degrees of course completion. Specifically, two categories of motivational aspects will be taken into account in this initial research attempt. Both MOOC-specific motivational items including those tested by existing MOOC studies (e.g., Belanger & Thornton, 2013; MOOC @ Edinburgh, 2013) and two subscales of the PALS survey (Midgley, et al., 2000) measuring mastery goal orientation and academic efficacy will be included in a pre-course survey. The MOOC-specific items include questions such as the familiarity of the MOOC environment and course content; whereas the PALS subscales focus on learner orientations regardless of the learning context.
Methods including learning analytics and educational data mining techniques will also be applied following analysis of survey results accordingly. Specifically, drawing from past research in monitoring online learning and community (Dawson, 2006; Ming & Ming, 2012), this project will monitor course participation including discussion forums, quiz completion rate as well as correction ratio. In addition, learner’s video engagement activities will be coded and recorded. All the above-mentioned data collected will thereafter be linked to the MOOC survey employing FDR post-hoc correction and correction mining methods. Patterns of changes in participation will also be monitored throughout the course via questionnaire responses by means of sequential pattern mining. A similar paradigm drawn from Bowers’ (2012) study predicting high school dropout from grades and Romero’ (2011) work in predicting college dropout will be analyzed and applied.


Ryan S.J.d. Baker

Teachers College Columbia U

Yuan Wang

Teachers College Columbia U

Friday December 6, 2013 11:30am - 12:00am
Meeting Room 12

Attendees (11)