Quick response codes for virtual learner evaluation of teaching and attendance monitoring
Bibliographic record
Abstract
Implication Statement Monitoring attendance and obtaining timely learner evaluations for virtual teaching sessions can be challenging. At our Obstetrics and Gynecology webinar programme, we have utilised Quick Response (QR) codes this purpose. Following each session, attendees scan an on-screen QR code which links to an online evaluation form and registers their attendance. Feedback can therefore be obtained quickly, is scalable to large participant numbers and is securely stored in digital format. QR reader applications are widely available and cost-free, which makes this technique accessible for learners. Using QR codes for teaching evaluation is simple and could be adopted across many educational applications. Énoncé des implications de la recherche Contrôler la présence des étudiants aux cours en ligne et obtenir en temps utile leur évaluation des séances d'enseignement virtuelles peut constituer un défi. Dans notre programme de webinaires en obstétrique et gynécologie, nous le faisons à l'aide de codes de réponse rapide (codes QR). Après chaque séance, les participants scannent un code QR qui apparaît sur leur écran; la lecture du code permet de confirmer leur présence et renvoie à un formulaire d'évaluation en ligne. Ce mécanisme rend possible la rétroaction rapide, la participation d'un grand nombre de personnes et la conservation sécuritaire de l'information en format numérique. Les applications de lecture de codes QR sont largement disponibles et gratuites, et donc accessibles aux étudiants. L'utilisation des codes QR pour évaluer les cours est simple et elle peut être intégrée dans de nombreuses applications éducatives.
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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 | 0.034 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.019 |
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".