Assessing Smart Glasses-based Foodservice Training: An Embodied Learning Theory Approach
Bibliographic record
Abstract
The present study evaluated simulated, hands on foodservice training delivered through smart glasses compared to passive, strictly video-based training. Handwashing performance variables, including frequency and efficacy, were measured along with post-training reactions. Participants in the strictly video-based group (N = 24) were four times more likely to wash hands than the smart glasses group (N = 25), (95% CI: 1.129 - 14.175). This research highlights how simulation training of handwashing with smart glasses can result in poorer learning outcomes compared to traditional training methods, potentially due to the psychological effects of hand cleansing. The observed training outcomes may also show the need to improve smart glasses-based training by finding ways to decrease attention demands and implementing augmented reality intelligence systems that can enforce training outcomes. Future research should utilize longitudinal studies to determine the impact of smart glasses-based training on food safety behavior habit formation. La présente étude a évalué une formation en service alimentaire active et appliquée, livrée par l’entremise de lunettes intelligentes, comparativement à une formation passive strictement basée sur la vidéo. Des variables de rendement relatives au lavage des mains ont été mesurées, y compris la fréquence et l’efficacité. Les participants du groupe dont la formation était strictement basée sur la vidéo (N = 24) étaient quatre fois plus susceptibles de se laver les mains que les participants du groupe aux lunettes intelligentes (N = 25), (95 % IC : 1,129 – 14,175). Les résultats soulignent que la formation par lunettes intelligentes dans laquelle les participants s’exercent à se laver les mains peut entraîner de moins bons résultats d’apprentissage que les méthodes de formation traditionnelles. Cela peut être dû à : a) la nature du contenu pédagogique, dans lequel la mémoire prospective intervient, comparativement à des études préalables avec l’apprentissage incarné et les lunettes intelligentes, qui ont évalué la mémoire rétrospective et les fonctions motrices, ou b) aux effets psychologiques sur la mémoire dont le groupe aux lunettes intelligentes aurait fait l’expérience durant la formation. Des études futures pourraient explorer l’effet de la formation par simulation à l’aide de lunettes intelligentes sur d’autres tâches de service alimentaire.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".