Text stream mining for Massive Open Online Courses: review and perspectives

Safwan Shatnawi, Mohamad Medhat Gaber, Mihaela Cocea

Research output: Contribution to journalArticlepeer-review

87 Downloads (Pure)

Abstract

Massive Open Online Course (MOOC) systems have recently received significant recognition and are increasingly attracting the attention of education providers and educational researchers. MOOCs are neither precisely defined nor sufficiently researched in terms of their properties and usage. The large number of students enrolled in these courses can lead to insufficient feedback given to the students. A stream of student posts to courses’ forums makes the problem even more difficult. Students’–MOOCs’ interactions can be exploited using text mining techniques to enhance learning and personalise the learners’ experience. In this paper, the open issues in MOOCs are outlined. Text mining and streaming text mining techniques which can contribute to the success of these systems are reviewed and some open issues in MOOC systems are addressed. Finally, our vision of an intelligent personalised MOOC feedback management system that we term iMOOC is outlined.
Original languageEnglish
Pages (from-to)664-676
JournalSystems Science Control Engineering
Volume2
Issue number1
Early online date31 Oct 2014
DOIs
Publication statusPublished - 2014

Fingerprint

Dive into the research topics of 'Text stream mining for Massive Open Online Courses: review and perspectives'. Together they form a unique fingerprint.

Cite this