Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective

Willis, James; Slade, Sharon and Prinsloo, Paul (2016). Ethical oversight of student data in learning analytics: a typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64(5) pp. 881–901.

DOI: https://doi.org/10.1007/s11423-016-9463-4

Abstract

The growth of learning analytics as a means to improve student learning outcomes means that student data is being collected, analyzed, and applied in previously unforeseen ways. As the use of this data continues to shape academic and support interventions, there is increasing need for ethical reflection on operational approvals for learning analytics research. Though there are clear processes for vetting studies resulting in publication of student-gathered data, there is little comparable oversight of internally generated student-focused research. Increasingly, ethical concerns about the collection and harvesting of student data have been raised, but there is no clear indication how to address or oversee these ethical concerns. In addition, staff members who are not typical researchers may be less familiar with approvals processes and the need to demonstrate potential for harm, etc. If current trends point to a range of individuals harvesting and analyzing student data (mostly without students' informed consent or knowledge), how can the real danger of unethical behavior be curbed to mitigate the risk of unintended consequences? A systematic appraisal of the policy frameworks and processes of ethical review at three research institutions (namely, the University of South Africa, the Open University in the United Kingdom, and Indiana University in the United States) provides an opportunity to compare practices, values, and priorities. From this cross-institutional review, a working typology of ethical approaches is suggested within the scope of determining the moral intersection of internal student data usage and application.

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