Text stream mining for Massive Open Online Courses:review and perspectives
| Autor: | Mohamad Medhat Gaber, Mihaela Cocea, Safwan Shatnawi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Control and Optimization
Computer science business.industry Massive open online course Intelligent decision support system Computing Data science World Wide Web Text mining Artificial Intelligence Control and Systems Engineering Management system ComputingMilieux_COMPUTERSANDEDUCATION business Text stream |
| Zdroj: | Shatnawi, S, Gaber, M M & Cocea, M 2014, ' Text stream mining for Massive Open Online Courses : review and perspectives ', Systems Science Control Engineering, vol. 2, no. 1, pp. 664-676 . https://doi.org/10.1080/21642583.2014.970732 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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