Abstract
The evaluation of MOOCs (massive open online courses) is needed to improve their design quality and to inform learners regarding course selection. In this paper, we proposed and validated an Analytic Hierarchy Process (AHP) Approach based on standardized rubric, expert feedback, data mining and emotion detection to systematically and diagnostically evaluate the quality of MOOCs. Using this approach, we analyzed review comments of three popular MOOCs on the Coursera Platform. The results indicate that the AHP approach is a feasible MOOC evaluation method that can provide accurate ratings as well as in-depth analysis of course design and learning outcomes. It is concluded that this new approach can supplement the existing user rating system with automated formative and summative evaluations.
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Acknowledgment
This study has been supported by the self-determined research funds of Central China Normal University from the colleges’ basic research and operation of MOE (CCNU19TD023) and the Basic Research Funding Grant of Central China Normal University (CCNU18QN023).
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Nie, Y., Luo, H. (2019). Diagnostic Evaluation of MOOCs Based on Learner Reviews: The Analytic Hierarchy Process (AHP) Approach. In: Cheung, S., Lee, LK., Simonova, I., Kozel, T., Kwok, LF. (eds) Blended Learning: Educational Innovation for Personalized Learning. ICBL 2019. Lecture Notes in Computer Science(), vol 11546. Springer, Cham. https://doi.org/10.1007/978-3-030-21562-0_24
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