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Student privacy and institutional accountability in an age of surveillance

Version 2 2024-01-05, 14:01
Version 1 2023-11-08, 11:23
posted on 2024-01-05, 14:01 authored by Sharon Slade, Paul Prinsloo
Optimizing the harvesting and analysis of student data promises to clear the fog surrounding key drivers of student success and retention, and our understanding of the impact of interventions to improve student success. While recognising its potential, it is crucial to deconstruct some of the assumptions regarding learning analytics in the nexus between student data and institutional accountability as well as broader concerns regarding the scope and impact of acts of surveillance. This chapter will explore and contest assumptions regarding the potential of big data in higher education – for example, that big data will, in itself, result in more accuracy or objectivity, that surveillance is uni-directional and that available data provides holistic pictures of student learning. Such assumptions not only impact directly on our view of institutional accountability towards a range of stakeholders, but also on the ethical implications of the harvesting of student data, its use and storage. Assumptions and understanding of the issues surrounding student privacy and institutional accountability are sedimented in institutional policies, operational frameworks based on a technocratic predictive logic inherent in neoliberal and governmentality discourses. Policies and frameworks in higher education institutions should provide enabling and ethical environments for the optimal use of learning analytics as negotiated and student centred praxis. This chapter explores student data privacy in the context of the dominant technocratic predictive logic in learning analytics. We propose a broader framework for the interrogation of learning analytics as an ethical and negotiated contract between institutions and students within an ethics of care.



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