Online Proctoring and Discrimination: A Critical Examination of Online Proctoring Technologies
Bibliographic record
Abstract
This exploratory research project investigates students’ negative experiences with online proctor- ing (OP) educational software (having a human proctor monitor a student in real time through their computer’s webcam or being recording with the software's artificial intelligence (AI) sys- tem), 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 re- search 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 three parts: (1) 46 anonymous surveys which were distributed to cur- rently registered students and recent alumni with graduation taking place between 2020 and 2024 at Queen’s University, Western University and Toronto Metropolitan University; (2) three in- depth qualitative interviews with a selection of survey respondents. Students widely reported in- vasive experiences and increased work burdens that intersected with pre-existing burdens due to structural inequities; (3) an analysis of 68 TikToks with the following hashtags: #onlineproctor, #onlineproctoring, #Examity, #Proctortrack, #onlineexam, #onlineexams, #digitalproctoring. Students widely reported invasive experiences and increased work burdens that intersected with pre-existing burdens due to structural inequities. Negative student experiences with OP technol- ogy fell into three categories: OP software as techno-solutionist endeavor, students’ concerns about surveillance and monitoring, and students facing discrimination from data driven educa- tional technologies. The impacts of university administrators using OP technology to replace in- person exams during remote learning include students having to expend additional labour and time when having to set up the exam space. OP software also led to students being discriminated or punished for facing class inequality, racism, sexism, ableism; or for mundane factors unrelated to academic integrity. By providing qualitative research, I hope to enhance understanding for the impact of OP technology on undergraduate university students in Ontario as well as deepen the understanding of the relationship between ed tech and inequality in Canadian education. Ulti- mately, this research also speaks to broader, growing concerns about the relationship between technology, justice and power due to COVID-19 "quick-fix" responses through how an individ- ual experience similar technologies differently depending on their positionality (Taylor et al. 2020: 12). Altogether, this will contribute to more comprehensive and equitable solutions to re- mote learning strategies in universities, while potentially improving access to education within the existence of ed-tech.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".