A Data Protection Framework for Learning Analytics

Most studies on the use of digital student data adopt an ethical framework derived from human-subject research, based on the informed consent of the experimental subject. However, consent gives universities little guidance on using 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 intervention. This paper proposes a new framework based on the approach used in 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. Organizations 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 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.