Since becoming involved in Jisc’s work on learning analytics, I’ve been trying to work out the best place to fit the use of students’ digital data to improve education into data protection law. I’ve now written up those thoughts as a paper, and submitted it to the Journal of Learning Analytics. As the abstract says:
Most studies on the use of digital student data adopt an ethical framework derived from human-studies research, based on the informed consent of the experimental subject. However consent gives universities little guidance on the use of learning analytics as a routine part of educational provision: which purposes are legitimate and which analyses involve an unacceptable risk of harm. Obtaining consent when students join a course will not give them meaningful control over their personal data three or more years later. Relying on consent may exclude those most likely to benefit from early interventions.
This paper proposes an alternative framework based on European Data Protection law. Separating the processes of analysis (pattern-finding) and intervention (pattern-matching) gives students and staff continuing protection from inadvertent harm during data analysis; students have a fully informed choice whether or not to accept individual interventions; organisations obtain clear guidance: how to conduct analysis, which analyses should not proceed, and when and how interventions should be offered. The framework provides formal support for practices that are already being adopted and helps with several open questions in learning analytics, including its application to small groups and alumni, automated processing and privacy-sensitive data.
As a current student and (twice) alumnus, I prefer to think that my own data will be handled according to these ideas, rather than on the basis that I gave informed consent mumble years ago.
The paper was published as Cormack, A. N. (2016). A Data Protection Framework for Learning Analytics. Journal of Learning Analytics, 3(1), 91–106. https://doi.org/10.18608/jla.2016.31.6
