MétaCan
Menu
Retour à la cohorte
Enregistrement W3176646641 · doi:10.1096/fasebj.2019.33.1_supplement.17.6

The Impact of a Novel Computer‐assisted Learning Resource on Student Learning in Undergraduate Dissection‐ and Prosection‐based Laboratory Environments

2019· article· en· W3176646641 sur OpenAlexaffabout
Sean McWatt, Genevieve Newton, Lorraine Jadeski

Notice bibliographique

RevueThe FASEB Journal · 2019
Typearticle
Langueen
DomaineEngineering
ThématiqueAnatomy and Medical Technology
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésResource (disambiguation)Thematic analysisMedical educationDissection (medical)PerceptionPsychologyMedicineComputer scienceAnatomyQualitative researchSociology

Résumé

récupéré en direct d'OpenAlex

Human anatomy is an essential subject for the medical and health sciences. Teaching and learning human anatomy each require large investments of time and resources; however, many institutions are challenged by limitations to both. As a result, the approaches used to teach human anatomy are constantly evolving to overcome these limitations and deliver meaningful learning opportunities. One common method for enhancing anatomy education is through computer‐assisted learning (CAL). The rapid growth and advancement of technology over recent decades has made the creation of CAL resources cheaper, easier, and more accessible, facilitating their rise as popular supplements to traditional approaches such as dissection (DI) and prosection (PRO). Accordingly, this study evaluated a novel CAL resource that was created for and introduced into an undergraduate DI and PRO human anatomy course at the University of Guelph between the Fall 2015 and 2016 semesters. The objective was to determine the influence of the resource on the students' academic experiences through evaluations of their course satisfaction (CS), contextual approaches to learning (SAL, characterized by deep [DA] and surface [SA] approach scores), and overall course performance. Participants reported their demographic information, CS, SAL, and use of the CAL resource through a combination of online and written surveys. Written feedback regarding their perceptions of the CAL resource was also collected and thematic analysis was performed to extract common themes. CS was compared between the Fall 2015 and Fall 2016 academic cohorts using the Mann‐Whitney U test. Comparisons of contextual SAL and performance were made using analyses of covariance with preferred SAL scores and cumulative grade averages as covariates, respectively. CAL resource use by students in both DI and PRO was then characterized and multiple linear regression analyses were used to determine correlations between their use of the resource and DA scores, SA scores, and course performance. Although the students' mean (± SEM) CS improved between the 2015 and 2016 cohorts in both DI (2015 = 74.15 ± 3.29, 2016 = 86.54 ± 1.94, p = 0.002) and PRO (2015 = 78.41 ± 5.95, 2016 = 93.55 ± 3.06, p = 0.051), the mean differences (± SEM) in DA (DI = −2.11 ± 0.53, p < 0.0005; PRO = −2.72 ± 1.03, p = 0.010) and SA (DI = 1.95 ± 0.55, p < 0.0005; PRO = 4.16 ± 1.10, p < 0.0005) scores suggested that the course presented a more surface‐oriented environment in 2016 than in 2015. Contextual SAL and course performance were not directly influenced by CAL resource use ( p > 0.05); however, students in both DI ( p = 0.001) and PRO ( p = 0.025) who reported higher positive perceptions of the resource had higher DA scores. These findings indicated that using the CAL resource did not significantly enhance the students' learning experience. However, those who saw value in the resource and reported more positive perceptions toward it used deeper approaches to learning, which are representative of meaningful learning. Alongside the analyses of written student feedback, this suggests that the context in which CAL resources are to be disseminated may merit strong consideration before incorporating such resources into a course. This abstract is from the Experimental Biology 2019 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,444
Score d'incertitude au seuil0,413

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,238
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueThe FASEB JournalMême sujetAnatomy and Medical TechnologyTravaux en français237 207