New piece in the British Journal of Educational Technology with Kirsty Kitto, accepted version available here. This one draws on our experience in developing learning analytics, the kind of practical ethics associated with virtue theories, and tensions in existing ethics approaches in learning analytics which we argue could be augmented by practical ethics.
Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12868
Abstract: Artificial intelligence and data analysis (AIDA) are increasingly entering the field of education. Within this context, the subfield of learning analytics (LA) has, since its inception, had a strong emphasis upon ethics, with numerous checklists and frameworks proposed to ensure that student privacy is respected and potential harms avoided. Here, we draw attention to some of the assumptions that underlie previous work in ethics for LA, which we frame as three tensions. These assumptions have the potential of leading to both the overcautious underuse of AIDA as administrators seek to avoid risk, or the unbridled misuse of AIDA as practitioners fail to adhere to frameworks that provide them with little guidance upon the problems that they face in building LA for institutional adoption. We use three edge cases to draw attention to these tensions, highlighting places where existing ethical frameworks fail to inform those building LA solutions. We propose a pilot open database that lists edge cases faced by LA system builders as a method for guiding ethicists working in the field towards places where support is needed to inform their practice. This would provide a middle space where technical builders of systems could more deeply interface with those concerned with policy, law and ethics and so work towards building LA that encourages human flourishing across a lifetime of learning.
Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12868. Author accepted version available for download here