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The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course

Cited 74 time in Web of Science Cited 90 time in Scopus
Authors

Jung, Eulho; Kim, Dongho; Yoon, Meehyun; Park, Sanghoon; Oakley, Barbara

Issue Date
2019-01
Publisher
Pergamon Press Ltd.
Citation
Computers and Education, Vol.128, pp.377-388
Abstract
Responding to the lack of empirical studies on the effects of instructional design components on MOOCs, this study explores which instructional design components (e.g., course content, transactional interaction between student and content, structure/organization, assessment) influence learner control, sense of progress in the achievement of learning goals (sense of progress), and perceived effectiveness in a large-scale MOOC course called "Learning How to Learn" hosted in Coursera, a MOOC learning platform. Using an online survey distributed to learners who registered for the current Coursera English-language version of the course, we collected 1364 responses. Three separate hierarchical regression analyses revealed that all course design factors, transactional interaction between student and content (beta=.111, p<0.01; beta=.117, p<.01), structure (beta=.432, p<0.001; beta=.281, p<.001), and assessment (beta=.108, p<0.01; beta=.102, p<.05) were significant predictors of learner control and sense of progress. For perceived effectiveness, only transactional interaction (beta=.073, p<0.05) and structure (beta=.416, p<0.001) were significant while assessment was not statistically significant (beta=.030, p=.373). These findings provided empirical evidence that instructional components are critical predictors of student learning in MOOCs, which have been conceptualized as important factors in prior studies. Future research should focus on identifying effective and efficient ways to facilitate assessments as part of the learning process while accommodating personalized learning needs. Interpretations of the findings, discussions, and limitations are also addressed in this paper.
ISSN
0360-1315
URI
https://hdl.handle.net/10371/219137
DOI
https://doi.org/10.1016/j.compedu.2018.10.001
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  • College of Agriculture and Life Sciences
  • Department of Vocational Education and Workforce Development
Research Area AI-Human Interaction, People Analytics, Technology-Based Career Development

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