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The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and 'datafying' students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner's control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity. [ABSTRACT FROM AUTHOR]

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and 'datafying' students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner's control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity. [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]