TWG 6: Putting learning back into learning analytics: optimizing learning through analysing the data

To target the outcomes of data systems is a new challenge for computer scientists and engineers as well as educators. For instance, learning analytics of student data sets can be used for formative and summative assessments, but issues related to privacy and usability are growing concerns. For example, with large data sets available to teachers and learners, who owns these data, which data are available and which are private? Furthermore, who analyses these data and who is the data analyzed for? What can teachers do with all these data and what feedback and monitoring of learning might students expect from learning analytics? How can fair uses of techno-led/enabled assessment (e.g. concept maps) be ensured and what are the risks associated with data use for promoting students’ achievements? The group will discuss how learning analytics can influence policy and teaching practices.

TWG 6 co-leaders: Jonathan San Diego (King's College London), David Gibson (Curtin University), Dirk Ifenthaler (University of Mannheim)

Link to TWG 6's Working document

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