Algorithmic governmentality and student subjectivities: a critical examination of learning analytics in higher education

This study critically explored the impact of learning analytics on students’ subjectivities in higher education. We introduce the perspective of algorithmic governmentality as a novel analytical lens for critical research in learning analytics, offering empirical insights into how students internalise, question or subvert subtle forms of technological guidance. Using a mixed qualitative methodology, we investigated the narratives of 103 students in a master’s programme in business education at an Austrian university. The study revealed fundamental ambivalences in students’ modes of subjectivation, oscillating between enthusiasm as well as resignation and anxiety. The introduction of learning analytics tends to result in a circumvention of reflexivity and an activation of self-regulation, which aligns students’ behaviours with data-driven norms. This engenders a restriction on the scope of action and the (re)production of educational inequalities. Conversely, this study indicates potential avenues for disruption and reflexive enquiry through students’ critical engagement with learning analytics.