A big data framework for early identification of dropout students in MOOC
Author(s)
Tang, Jeff Kai Tai
Xie, Haoran
Wong, Tak Lam
Date Issued
2015
Publisher
Springer
Related Publication(s)
Technology in education: Technology-mediated proactive learning - Revised selected papers of the 2nd International Conference (ICTE 2015)
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.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.