Editorial: Datasets for Learning Analytics


  • Stefan Dietze L3S Research Center University of Hannover, Germany
  • George Siemens LINK Research Lab University of Texas Arlington
  • Davide Taibi Institute for Educational Technology National Research Council of Italy
  • Hendrik Drachsler Open University of the Netherlands




The European LinkedUp and LACE (Learning Analytics Community Exchange) project have been responsible for setting up a series of data challenges at the LAK conferences 2013 and 2014 around the LAK dataset. The LAK datasets consists of a rich collection of full text publications in the domain of Learning Analytics and Educational Data Mining. The LAK dataset offers publicly available, machine-readable versions of research articles from the Learning Analytics and Educational Data Mining communities in various formats, where the main goal is to facilitate research, analysis, and smart explorative applications. Based on the insights gained from these data challenges, the idea was born to make more Learning Analytics data sets publicly available for researchers to get to a more open access data-driven research community within Learning Analytics. With this special section, we publish four data sets that answered the call for data sets by the journal. It is our vision to collect more data sets like these in this initial collection and reward their creators through citing the datasets and connecting new research outcomes to them.




How to Cite

Dietze, S., Siemens, G., Taibi, D., & Drachsler, H. (2016). Editorial: Datasets for Learning Analytics. Journal of Learning Analytics, 3(2), 307-311. https://doi.org/10.18608/jla.2016.32.15



Special section: Dataset Descriptions for Learning Analytics

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