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Enregistrement W3017296392 · doi:10.1096/fasebj.2020.34.s1.03368

Undergraduate Student’s Preferred Learning Modality for Flipped Anatomy Laboratory Sessions

2020· article· en· W3017296392 sur OpenAlex

Pourquoi ce travail est dans la base

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
aboutLe titre ou le résumé porte un signal canadien du lexique géographique.

Notice bibliographique

RevueThe FASEB Journal · 2020
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueInnovative Teaching Methods
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésSession (web analytics)Flipped classroomModality (human–computer interaction)Reading (process)ModalitiesClass (philosophy)Blended learningMultimediaMathematics educationComputer sciencePsychologyMedicineMedical educationEducational technologyArtificial intelligenceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Introduction The flipped classroom approach (FCA) is a growing instructional method in higher education. In the flipped classroom, students’ first exposure to content is prior to class, commonly in the form of two learning modalities (LMs): a textbook reading, or a video recording. Class time then focuses on the application of knowledge. This approach has been implemented in many lecture‐based courses; however, it has yet to be fully evaluated in the anatomical laboratory (lab). It remains unclear whether students prefer traditional or flipped instruction for anatomy lab sessions and, with a FCA, which LM provides anatomical content exposure that most aligns with student preferences. Thus, the aims of this cross‐over study were: (i) to determine students’ preferred instruction type for anatomy lab sessions, (ii) to determine students’ rank‐order of learning modalities for anatomical content exposure from most to least preferred, and (iii) to establish the reasons for learning modality preferences. Methods Undergraduate students (n=59) were recruited from a systemic human anatomy course at the University of Western Ontario and were placed in one of four study groups. The groups were exposed to three flipped and one traditional lab sessions, with each session focused on a different musculoskeletal region. The flipped lab sessions used one of the following LMs for content exposure before the in‐lab component of the session: a textbook reading, a video recording, or a three‐dimensional (3D) anatomy application (app). The traditional lab sessions did not have content exposure before the in‐lab component but consisted of a lab talk during the in‐lab session using cadaveric specimens. An open‐ended questionnaire determined student’s preferred instruction type and rank‐order preference for the LMs. Open‐ended questions were coded and analyzed for themes to establish the reasons for the student’s rank‐order of the LMs. Results (i) The majority of students (57.6%) preferred a LM associated with flipped instruction compared to that associated with the traditional instruction. (ii) The average ranking of the LMs from most to least preferred were lab talk, 3D anatomy app, video recording, and textbook reading, with the textbook reading most often ranked as the least preferred LM (p < 0.05). (iii) Based on responses to the open‐ended questions, many students preferred LMs that allowed for representational visualization of the anatomical structures. Majority of students also preferred LMs, which allowed them to control their pace of learning. Inadequate visualization of the structures and lack of engagement were the main reasons the textbook reading was predominately ranked as the least preferred LM. Conclusion The FCA relies heavily on student’s gaining a baseline of knowledge before class time; therefore, it is important to determine the LMs that enhance engagement, and most align with student learning preferences to promote completion of content exposure before class. This study revealed that anatomy students prefer LMs that promote visualization of the structures and allow for control of the pace of learning. Thus, when designing LMs to expose undergraduate students to the anatomical content, consideration should be given to the incorporation of the identified preferences to maximize student engagement.

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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)0,006
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,400
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0010,000
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 Opus0,095
Tête enseignante GPT0,428
Écart entre enseignants0,332 · 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