"This is not a data problem": Algorithms and Power in Public Higher Education in Canada
Kelly McConvey, Shion Guha
TL;DR
This paper investigates how a public college in Ontario, Canada, deploys data-driven and algorithmic decision-making within a neoliberal, fiscally constrained governance context. Using an ethnographic case study and the ADMAPS framework, it maps data sources, three active models (including an Early Alert System), and the institutional processes surrounding data use. The findings reveal increased student surveillance, potential bias and inequities, and a shift of power toward data-centric divisions, culminating in the ASP-HEI Cycle of Algorithms, Student Data, and Power in Higher Education Institutions. The work argues for holistic assessments and human discretion to counterbalance automation-driven risks and to realign practices with inclusive, student-centered goals.
Abstract
Algorithmic decision-making is increasingly being adopted across public higher education. The expansion of data-driven practices by post-secondary institutions has occurred in parallel with the adoption of New Public Management approaches by neoliberal administrations. In this study, we conduct a qualitative analysis of an in-depth ethnographic case study of data and algorithms in use at a public college in Ontario, Canada. We identify the data, algorithms, and outcomes in use at the college. We assess how the college's processes and relationships support those outcomes and the different stakeholders' perceptions of the college's data-driven systems. In addition, we find that the growing reliance on algorithmic decisions leads to increased student surveillance, exacerbation of existing inequities, and the automation of the faculty-student relationship. Finally, we identify a cycle of increased institutional power perpetuated by algorithmic decision-making, and driven by a push towards financial sustainability.