Online Proctoring of High-Stakes English Language Examinations: A Survey of Past Candidates’ Attitudes and Perceptions
Bibliographic record
Abstract
This paper reports reactions by candidates to the use of online proctoring (OLP), ‘invigilation’, in the delivery of high-stakes English language examinations.  The paper first sets the scene in terms of the move from face-to-face to online modes of delivery. It explores the challenges and benefits that both modes offer, in terms of accessibility, fairness, security and cheating. Evidence is then presented from a survey exploring the reactions to and perceptions of OLP by candidates who had taken an English language examination via OLP. A strong endorsement of OLP was generally recorded. Feedback revealed that respondents perceived OLP to be a more personal as well as a more efficient way of taking a test. Some pertinent negative comments from a smaller number of respondents could be construed as constructive and are also discussed. The results are indicative of a broad acceptance of OLP, pointing to strong future uptake of the OLP mode of test delivery.   
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".