Dissection‐based Audiovisual Modules to Supplement Laboratory‐based Human Anatomy Education
Notice bibliographique
Résumé
The notion of “doing more with less” is ubiquitous. At the University of Guelph, over 500 undergraduate students attend the human anatomy program yearly. Courses are accompanied by weekly dissection‐ and prosection‐based laboratories. Dissection‐based audiovisual modules were developed to ameliorate the high student to teaching assistant (TA) ratio, improve laboratory efficiency, promote deep learning (DL), and ultimately improve the overall educational experience. Through high‐definition videography, photography, and anatomical illustration, the modules capture a regional‐based dissection for the first 12 weeks of the course (back, upper limb, thorax, and abdomen). Each module is comprised of three chapters; 1) an introduction, presenting learning outcomes and objectives of the laboratory; 2) an overview, using hyper‐lapse videography to demonstrate the full dissection in a minute; 3) the main chapter, presenting the dissection procedure and techniques to achieve laboratory goals, along with important anatomical relationship and concepts. The modules are packaged in a user‐friendly, multiplatform package, offering interactive quizzes, supplementary resources, and user tracking. The revised two‐factor study process questionnaire (RSPQ‐2F) was used to measure students' approach to learning. An extended version of the course experience questionnaire (CEQ) was used to quantify the students' subjective course experiences. Weekly laboratory exit surveys were used to quantify the students' module usage characteristics. Additional data such as age, sex, and overall GPA average were collected during the semester. Lastly, focus group sessions were conducted with students and TAs. Dissection students utilized the modules 240% more than prosection students over the length of the semester (Di: 110.2 ± 8.17 min, Pro: 45.8 ± 10.8 min, t(73.22) = 4.76, p = < .0005, d = .54) (Fig. 1). Morning dissection laboratories which typically initiate new dissections utilized the modules 186% more than the afternoon dissection laboratories (AM: 143.6 ± 14.4 min, PM 77.2 ± 6.7 min, 45.8 ± 10.8 min, t(173.94) = 3.96, p = < .0005, d = 0.50). Module usage peaked during the deep back and axilla dissections, and dipped during the practical exam review laboratories, suggesting students focused on reviewing from the donors (Fig. 2). Lastly, as students became more self‐directed throughout the semester, module usage was reduced (Fig. 2). Overall CEQ score increased by 7.93 points when compared to the previous year (t(228) = 3.71, p < .0005, d = 0.25). The highest categorical increase was attributed to the “Clear Goals and Standards” scale, with a 20.35 point increase (t(228) = 5.84, p < .0005, d = 0.39). There was no significant correlation between module usage and deep approach scores. However, positive correlation was observed between module usage and the deep strategy subscale (r s (242) = 0.155, p = .023). TA focus groups suggests students relied less on the TAs for basic identification and guidance questions, providing TAs with more time to explain important anatomical relationship and concepts. Responses for student focus groups and surveys suggests expedience of this tool and need for online availability as a pre‐laboratory study resource. Overall, data suggests that dissection‐based modules are an effective supplementary tool for laboratory‐based human anatomy education. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,070 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».