Data Sharing for Learning Analytics: Exploring Risks and Benefits through Questioning

Jon Mason, Tore Hoel, Weiqin Chen

    Research output: Contribution to journalArticlepeer-review


    Moving learning analytics from the research labs to the classrooms, lecture halls, and digital learning spaces requires data sharing. Routine production and consumption of data is no limited to controlled settings as data can be sourced from an increasing diversity of data points and combined or aggregated from these sources within and often beyond the institution. This scaling up of learning analytics raises a host of questions on behalf of the data subjects and therefore informs requirements for design of new solutions and practices. This paper analyses a corpus of more than 250 questions gathered by a European project that organized international workshops and facilitated community exchange. It explores how these questions could inform both the problem space of data sharing and the solution space yet to be fully scoped by research and development within this emerging field. The analysis shows that the discourse on data sharing and big data for education is still at an early stage. Conceptual issues dominate this discourse; however, the elicited questions also hold numerous challenges for technical development and implementation. In concluding we propose a short inventory of what the emerging solution space may entail and suggest a path for further work.
    Original languageEnglish
    Article number1
    Pages (from-to)1-13
    Number of pages13
    JournalJournal of the Society of E-Learning
    Issue number1
    Publication statusPublished - Dec 2016


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