Building Training Methodology: Preparing Invigilators for Active, In-person, Exam Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 it