Do Dissection‐ and Prosection‐based Laboratories Offer Comparable Learning Experiences? An Exploration of Student Learning in Two Laboratory Cohorts at the University of Guelph
Notice bibliographique
Résumé
Historically, human anatomy has been taught using lectures and dissection‐based (DI) cadaver laboratories. However, many institutions face challenges such as limited financial resources, inadequate cadaver availability, and curricular time constraints that limit the use of DI laboratories. Consequently, prosection‐based (PRO) environments have been favoured as less resource‐intensive alternatives to DI. The benefits and drawbacks of using either DI or PRO have been debated for decades; however, since most institutions employ only one of these instructional environments, direct quantitative comparisons are rarely available. The University of Guelph offers a comprehensive human anatomy course in the third‐year of the Human Kinetics and Biomedical Science undergraduate degree programs that includes both DI and PRO cohorts. All students attend the same lectures and complete the same examinations, but are enrolled in either a DI or PRO laboratory. PRO students learn from the donors dissected by their DI peers, and therefore witness a ‘slow reveal’ of structures throughout the course. In the present study, course experience (CE), student approach to learning (SAL), and course performance, were compared between students enrolled in DI (n = 147) and PRO (n = 44). CE was measured using the course experience questionnaire (CEQ), preferred (p‐) and contextual (c‐) deep (DA) and surface (SA) learning approaches were measured using the revised two‐factor study process questionnaire (R‐SPQ‐2F), and grades on laboratory tests (LT), written tests (WT), and in‐laboratory oral assessments (LOA) were analyzed alongside final course grades as performance outcomes. No significant differences in scores on any CEQ subscale were found between students in DI or PRO ( p > 0.05). There was no significant main effect of laboratory type on SAL [ F (1,189) = 0.119, p = 0.731, partial η 2 = 0.001], but PRO students reported significantly lower cSA scores than pSA scores [ F (1,43) = 5.89, p = 0.020, partial η 2 = 0.120] and DI students had significantly higher cDA scores than pDA scores [ F (1,146) = 21.55, p < 0.0005, partial η 2 = 0.129]. Furthermore, the main effect of SAL type indicated that both groups adopted significantly higher DA scores than SA scores [DA = 34.95 ± 0.494, SA = 23.06 ± 0.480, F (1,189) = 181.54, p < 0.0005, partial η 2 = 0.490]. Multiple linear regression (MLR) analyses with demographic data, preferred SAL scores, and CEQ subscale scores as covariates revealed that although laboratory type did not significantly influence the prediction of cSA scores ( p = 0.413), participation in DI was positively associated with cDA score (β = 1.474 ± 0.724, p = 0.043). There were no significant differences in grades on LTs or WTs between DI and PRO students at p ≤ 0.05; however, DI students performed significantly better on LOAs than PRO students (DI = 91.96% ± 0.357, PRO = 87.09% ± 0.829, p < 0.0005) and had higher resulting final grades (β = 1.842 ± 0.885, p = 0.039). These findings suggest that DI and PRO laboratories both foster stronger DA than SA to learning, thus PRO may serve as an acceptable method of human anatomy instruction at institutions with limited resources. However, DI may better promote skills used in oral assessment such as communication, teamwork, and problem‐solving. 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».