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Aggregating social and usage datasets for learning analytics: Data-oriented challenges

: Niemann, K.; Wolpers, M.; Stoitsis, G.; Chinis, G.; Manouselis, N.


Association for Computing Machinery -ACM-:
Third International Conference on Learning Analytics and Knowledge, LAK 2013. Proceedings : Leuven, Belgium, 8-12 April 2013
New York: ACM, 2013
ISBN: 978-1-4503-1785-6
International Conference on Learning Analytics and Knowledge (LAK) <3, 2013, Leuven>
Fraunhofer FIT ()

Recent work has studied real-life social and usage datasets from educational applications, highlighting the opportunity to combine or merge them. It is expected that being able to put together different datasets from various applications will make it possible to support learning analytics of a much larger scale and across different contexts. We examine how this can be achieved from a practical perspective by carrying out a study that focuses on three real datasets. More specifically, we combine social data that has been collected from the users of three learning portals and reflect on how they should be handled. We start by studying the data types and formats that these portals use to represent and store social and usage data. Then we develop crosswalks between the different schemas, so that merged versions of the source datasets may be created. The results of this bottom-up, hands-on investigation reveal several interesting issues that need to be overcome before aggreg ated sets of social and usage data can be actually used to support learning analytics research or services.