Abstract
Massive Open Online Courses (MOOC) became popular and they posted great impact to education. Students could enroll and attend any MOOC anytime and anywhere according to their interest, schedule and learning pace. However, the dropout rate of MOOC was known to be very high in practice. It is desirable to discover students who have high chance to dropout in MOOC in early stage, and the course leader could impose intervention immediately in order to reduce the dropout rate. In this paper, we proposed a framework that applies big data methods to identify the students who are likely to dropout in MOOC. Real-world data were collected for the evaluation of our proposed framework. The results demonstrated that our framework is effective and helpful.
The original version of this chapter was revised. An erratum to this chapter can be found at 10.1007/978-3-662-48978-9_27
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-662-48978-9_27
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Notes
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https://www.coursera.org/about/community, retrieved on 1 September 2015.
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We employed Weka, which is an open-source data mining tools to implement our framework. (URL: http://www.cs.waikato.ac.nz/ml/weka/).
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Acknowledgement
This work is partially supported by the Small Research Grant (MIT/SRG10/14-15) and the Internal Research Grant (IRG 30/2014-2015) of the Hong Kong Institute of Education.
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Tang, J.K.T., Xie, H., Wong, TL. (2015). A Big Data Framework for Early Identification of Dropout Students in MOOC. In: Lam, J., Ng, K., Cheung, S., Wong, T., Li, K., Wang, F. (eds) Technology in Education. Technology-Mediated Proactive Learning. ICTE 2015. Communications in Computer and Information Science, vol 559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48978-9_12
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DOI: https://doi.org/10.1007/978-3-662-48978-9_12
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