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The popularity of Massive Open Online Courses (MOOCs) has attracted considerable attention from academic institutions providing the courses, potential students and researchers. The enthusiasm for all the possibilities of this type of online education has, however, been tempered by issues such as of the quality of education provided, the support needed by vast numbers of students and the high drop-out rate. The Educational Data Mining community has an important role to play in the debate about the advantages and disadvantages of MOOCs, as well as in proposing intelligent solutions for addressing various educational aspects. There are many challenges of knowledge discovery in MOOCs, including the vast volume of data and the diversity of users. These challenges, however, bring opportunities to develop new data mining techniques or adapt established knowledge discovery approaches to the requirements of analysing MOOCs data.
Via Peter B. Sloep
Last week I reported on the EMOOCs conference, in which a significant part was reserved for reporting on various experiences with and research results on MOOCs. This July a workshop will be held focussing on data mining in MOOCs. If MOOCs are as massive as their name suggests - which of course is not always the case in actual fact - then data mining should be particularly profitable. It should give us insights in how MOOCs fare but also on how to generate the raw material on which recommenders may operate. The call for papers is still open until April 14th, so everybody who has a data mining & MOOCs axe to grind, pay attention! @pbsloep