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This article proposes a pragmatic approach to data justice in education that draws upon Nancy Fraser's theory. The main argument is premised on the theoretical and practical superiority of a deontological framework for addressing algorithmic bias and harms, compared to ethical guidelines. The purpose of a deontological framework is to enable the evaluation of justice claims, not as a burden for individuals or groups, but as part of an ongoing public dialogue about the disparate impacts of algorithmic decisions. In a deontological framework, scrutiny is not limited to differences in treatment but is broader and more demanding, involving the evaluation of design approaches and a wider-ranging political discussion about who might be harmed.

This article proposes a pragmatic approach to data justice in education that draws upon Nancy Fraser's theory. The main argument is premised on the theoretical and practical superiority of a deontological framework for addressing algorithmic bias and harms, compared to ethical guidelines. The purpose of a deontological framework is to enable the evaluation of justice claims, not as a burden for individuals or groups, but as part of an ongoing public dialogue about the disparate impacts of algorithmic decisions. In a deontological framework, scrutiny is not limited to differences in treatment but is broader and more demanding, involving the evaluation of design approaches and a wider-ranging political discussion about who might be harmed. [ABSTRACT FROM AUTHOR]

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a 'performative device' that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed. [ABSTRACT FROM AUTHOR]