Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/780
Title: A big data framework for early identification of dropout students in MOOC
Author(s): Tang, Jeff Kai Tai 
Xie, Haoran 
Wong, Tak Lam 
Issue Date: 2015
Publisher: Springer
Related Publication(s): Technology in Education: Technology-Mediated Proactive Learning (Second International Conference, ICTE 2015) Revised Selected Papers
Start page: 127
End page: 132
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.
URI: https://repository.cihe.edu.hk/jspui/handle/cihe/780
DOI: 10.1007/978-3-662-48978-9_12
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

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