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Enregistrement W3016778887 · doi:10.1096/fasebj.2020.34.s1.03020

Are Western Active Learning Spaces Worth the Investment?

2020· article· en· W3016778887 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueThe FASEB Journal · 2020
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueInnovative Teaching Methods
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésActive learning (machine learning)Mathematics educationClass (philosophy)Likert scaleQualitative propertyFocus groupComputer sciencePsychologyPedagogySociologyArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

Although active learning is coming to the forefront of education, the physical infrastructure of classrooms has been relatively slow to change. Certain institutions, including Western University, have recently developed active learning spaces. These learning spaces often include movable furniture and numerous writing spaces, which creates a more interactive learning environment for students. They may range in size and set‐up, but one key feature that these spaces share is a rich opportunity for student collaboration. Previous studies have shown that students perceive active learning spaces as more engaging than traditional lecture spaces; however, the effect of learning spaces on transferable skill development, specifically effective communication, has not yet been investigated. Thus, the main objective of the current study was to evaluate the efficacy of Western’s Active Learning Spaces (WALS) in supporting student development of effective communication as a transferable skill. We recruited students from a fourth‐year undergraduate medical science course (N=33) to participate. The students started the Fall term of 2019 in a fixed‐row classroom and moved to a WALS at the midpoint of the term. The same instructor taught in both classroom environments and the course was designed as a flipped classroom where students are expected to complete online learning modules before coming to class. The current study utilized an explanatory sequential mixed‐methods approach to collect data. Quantitative data was collected first in the form of Likert surveys and classroom observation, followed by qualitative data collection in the form of focus groups. Students were observed using a previously validated instrument entitled, Classroom Observation Protocol for Undergraduate Science Technology Engineering Mathematics (STEM) (COPUS). Our observational COPUS data have shown that the instructor successfully created an active learning environment in both the fixed‐row classroom and the WALS and was using similar active learning activities in both environments. Students were asked to complete a Likert survey that measured communication apprehension at the start of the course and after exposure to each classroom setting. In addition, students were asked to complete a Likert survey to rate the impact each classroom had on overall classroom climate and learning. Finally, students were asked to complete a Likert survey indicating their classroom preference and the suitability of each classroom on a variety of items. Our results suggested that many students perceive themselves as having low communication apprehension and we found no significant difference in student responses between the start of the term and after exposure to the fixed‐row classroom. This is likely because students in the fourth year of their undergraduate degree have had exposure to active learning strategies and opportunities to develop their communication skills outside of the current course. Further data collection in the WALS is needed; however, based on other preliminary findings, we predict students will prefer the WALS and will perceive an increase in communication skills after WALS exposure.

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.

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)0,003
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: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,311
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
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 Opus0,103
Tête enseignante GPT0,368
Écart entre enseignants0,265 · 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