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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".