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Non-expert users find complex to gain richer insights into the increasingly amount of available heterogeneous data, the so called big data. Advanced data analysis techniques, such as data mining, are difficult to apply due to the fact that (i) a great number of data mining algorithms can be applied to solve the same problem, and (ii) correctly applying data mining techniques always requires dealing with the inherent features of the data source. Therefore, we are attending a novel scenario in which non-experts are unable to take advantage of big data, while data mining experts do: the big data divide. In order to bridge this gap, we propose an approach to offer non-expert miners a tool that just by uploading their data sets, return them the more accurate mining pattern without dealing with algorithms or settings, thanks to the use of a data mining algorithm recommender. We also incorporate a previous task to help non-expert users to specify data mining requirements and a later task in which users are guided in interpreting data mining results. Furthermore, we experimentally test the feasibility of our approach, in particular, the method to build recommenders in an educational context, where instructors of e-learning courses are non-expert data miners who need to discover how their courses are used in order to make informed decisions to improve them.

Abstract. With the increasing popularity of social networking services like Facebook or Twitter, social network analysis has emerged again. Discovering the underlying relationships between people-as well as the reasons why they arise or the type of those interactions-and measuring their influence are examples of tasks that are becoming to be paramount in business. However, this is not the only field of application in which the use of social network analysis techniques might be appropriate. In this paper, we expose how social network analysis can be a tool of considerable utility in the educational context for addressing difficult problems, e.g., uncovering the students' level of cohesion, their degree of participation in forums, or the identification of the most influential ones. Furthermore, we show that the correct management of social behavior data, along with the use of the student activity, helps us build more accurate performance and dropout predictors. Our conclusions are drawn from the analysis of an e-learning course taught at the University of Cantabria for three consecutive academic years.