Online Proctoring and Discrimination: A Critical Examination of Online Proctoring Technologies
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,015 | 0,021 |
| Communication savante | 0,012 | 0,014 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».