Leveraging Non-explicit Social Communities for Learning Analytics in Mobile Remote Laboratories


When performing analytics on educational datasets, the best scenario is where the dataset was designed to be analyzed. However, this is often not the case and the data extraction becomes more complicated. This contribution is focused on extracting social networks from a dataset which was not adapted for this type of extraction and where there was no relation among students: a set of remote laboratories where students individually test their experiments by submitting their data to a real remote device. By checking which files are shared among students and submitted individually by them, it is possible to know who is sharing how many files with who, automatically extracting what students are bigger sources. While it is impossible to extract the full real social network of these students, all the edges found are clearly part of it. These relations can indeed be used as a new input for performing the analytics on the dataset.