Research Title: Stream Data mining for Massive Open Online Courses
Start date: October 2013
Massive Open Online Courses (MOOCs) 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 students 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 research we aim to find answer the following research questions:
- What are the natural language processing (NLP) techniques to support text stream mining for MOOCs?
- What are the most powerful text stream mining techniques for MOOCs setting?
- How do text stream mining techniques contribute to the success of MOOCs by providing effective formative feedback?
- What are the effects of automatic formative feedback on engagement and performance of students in MOOCs?
Shatnawi, S., Gaber, M and Cocea, M. iMOOC: An Intelligent MOOCs Feedback Management System Architecture. The 8th European Conference on Technology Enhanced Learning, EC-TEL
Text stream mining for Massive Open Online Courses: review and perspectives, Safwan Shatnawi, Mohamad Medhat Gaber, Mihaela Cocea, Systems Science & Control Engineering, Vol. 2, Iss. 1, 2014.
Automatic Content Related Feedback for MOOCs Based on Course Domain Ontology,Shatnawi, Safwan and Gaber, Mohamed Medhat and Cocea, Mihaela, Intelligent Data Engineering and Automated Learning – IDEAL 2014, pp 27-35, Springer International Publishing 2014.