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Enregistrement W4389023847 · doi:10.1096/fasebj.31.1_supplement.580.12

The Anatomy of Traditional and E‐Learning Education: How the spatial ability of learners can impact learning outcomes

2017· article· en· W4389023847 sur OpenAlexaffabout
Sonya E. Van Nuland, Kem A. Rogers

Notice bibliographique

RevueThe FASEB Journal · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueAnatomy and Medical Technology
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésKinesthetic learningSpatial abilityTask (project management)CognitionPerspective (graphical)PsychologyComputer scienceMultimediaMathematics educationArtificial intelligenceNeuroscienceEngineering

Résumé

récupéré en direct d'OpenAlex

Technological innovation is changing the landscape of higher education, and the competing interests and responsibilities of today's learners have propelled the movement of post‐secondary courses into the online environment. In the anatomical sciences, computer‐aided instruction and online learning tools have become a critical component of teaching the intricacies of the human body when physical classroom space and cadaveric resources are limited. Our previous research (n=70) compared a simple 2‐dimensional e‐learning tool (A.D.A.M. Interactive Anatomy) to a more complex tool that allows for a more 3‐dimensional perspective (Netter's 3D Interactive Anatomy). Despite the differences in how these e‐learning tools present information, student ability to learn anatomical material, and their mental effort while doing so, known as cognitive load, were identical between e‐learning tools. However, when students with low spatial ability studied anatomical content with the more complex tool (Netter's 3D Interactive Anatomy), their performance scores were significantly lower than those students with high spatial ability (p=0.007). These results indicate that e‐learning tool software design can differentially influence students based on their spatial ability, but it remains to be determined if traditional kinesthetic‐tactile learning approaches, such as manipulating a skeleton, are also impacted by a student's spatial ability. Using a novel dual‐task methodology with a cross over design, undergraduate anatomy students from The University of Western Ontario, Canada (n=71) were evaluated as they studied a bony joint using a physical skeleton as well as a simple commercial software program (A.D.A.M. Interactive Anatomy). We hypothesized that the acquisition of anatomical knowledge by students, regardless of their spatial ability, would be superior when learning is associated with a real model, rather than currently available e‐learning tools. Students were assessed using a baseline knowledge test, Stroop observation task response times (a measure of cognitive load), MRT scores (a measure of spatial ability) and an anatomy post‐test (a measure of learning). Results suggested that while students may experience more cognitive load while studying using a physical skeleton (p<0.001), it does not detrimentally impact their performance; in fact student performance was significantly higher when they studied using the skeleton (p<0.001, R=0.46). Furthermore our results also demonstrated that students with low spatial ability are at a significant disadvantage when they learn the bony anatomy of a joint and are tested on images of the contralateral joint (p=0.023, R=0.326). This study highlights a major weakness in the strategy to move traditional anatomical education online, and suggests that students should be taught the anatomy of both sides of the human body, regardless of the reality that human limbs are mirror images of each other. These results can be further applied to the training of future surgeons and medical specialists, where surgical and medical procedures should be taught and practiced on both sides of the human body, to ensure that all students, regardless of spatial ability, can take their anatomical knowledge into the clinic and perform successfully. Support or Funding Information Social Science and Humanities Research Council, Government of Canada

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,687
Score d'incertitude au seuil0,705

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
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 Opus0,016
Tête enseignante GPT0,273
Écart entre enseignants0,257 · 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'étudeObservationnel
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

Citations0
Publié2017
Routes d'admission2
Résumé présentoui

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