Building Training Methodology: Preparing Invigilators for Active, In-person, Exam Management
Bibliographic record
Abstract
This study assesses effective training methods that support in-person, post-graduate, exam invigilators to build awareness of institutional policies as well as heighten their comfort and confidence with invigilating in the exam setting. Vigilant, active invigilators are considered effective in reducing student cheating behaviour on exams (Alabi, 2014; Attoh Odongo et al., 2021; Feng & Ouyang, 2021; Siniver, 2013). This study followed 26 exam invigilators of varying experience through pre-training, training, and post-exam invigilation. Invigilators completed an online survey prior to participating in an in-person, half-day training session, self-identifying existing levels of experience, policy knowledge, and comfort/confidence in the exam setting in numerous situations. Upon completion of an in-person training session in a group setting, they completed a second online survey, which showed overall improvement. Invigilators were then assigned a live, in-person invigilation shift and following this, completed a third online survey. The study concludes that the training methods implemented foster confident and capable exam invigilators who support students’ compliance with academic integrity. With the shift to online testing during the COVID-19 pandemic, consideration needs to be given as to whether in-person invigilators retain the knowledge when they experience lengthy lapses of employment, and how their learned skills may be transferable to the online environment.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".