Abstract
The popularity of Massive Open Online Courses has been rapidly growing recently. However, the completion rates of MOOC appear to be quite low. Moreover, the learning quality is quite doubtful for administrators of Universities since there is no suitable tools to evaluate it. Benefitting from the online environment, MOOC platforms can collect and store a huge amount of data related to learning processes. We use Storm as the parallel computing tool to accomplish the data analysis of MOOC. Our research focuses on three types of learning quality evaluation: relationship between students’ forum participation and their academic performance, relationship between students’ forum emotion and their academic performance, relationship between students’ video seeking operation and their academic performance.
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Acknowledgment
This paper is supported by Ministry of Education - China Mobile Research Fund of China under Granted No. MCM20150605.
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Zhao, Z., Wu, Q., Chen, H., Wan, C. (2017). Learning Quality Evaluation of MOOC Based on Big Data Analysis. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_28
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DOI: https://doi.org/10.1007/978-3-319-52015-5_28
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