Long abstract:

This exploratory research project investigates algorithmic bias in online proctoring (OP) educational software, and if and how undergraduate university students in Ontario face discrimination by data driven educational OP technologies. Although online proctoring technologies like Examity and Proctortrack have been widely discussed publicly for their consequences for educational equity – for example, students have reported that OP were not able to detect dark skin tones, erroneously flag neurodivergent students with accommodations, and more – there is very little empirical research which systematically documents the actual diversity of discriminatory effects students have experienced during the pandemic. This project used a cross-sectional design approach which I have conducted in two parts: (1) an anonymous survey which was distributed to currently registered students and recent alumni with graduation taking place between 2020 and 2024 at Queen’s University, Western University and Toronto Metropolitan University; (2) in-depth qualitative interviews with a selection of survey respondents. Students widely reported invasive experiences and increased work burdens that intersected with pre-existing burdens due to structural inequities. I conclude by situating my findings within broader, growing concerns about the relationship between technology, justice and power due to "quick-fix" responses to structural problems in higher education.