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Enregistrement W4398166638 · doi:10.1152/physiol.2024.39.s1.2356

Promoting Deep Learning In First-Year Physiology Through Structured In-class Activities: Building a Bridge for Success

2024· article· en· W4398166638 sur OpenAlexaff
Angela Beye, Anita Woods

Notice bibliographique

RevuePhysiology · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueInnovative Teaching Methods
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésBridge (graph theory)Class (philosophy)BiologyPhysiologyPsychologyComputer scienceAnatomyArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

The transition from high school to university is often a diffcult journey for learners. From data we have collected (unpublished), many students report successful use of surface learning strategies in secondary school, such as highlighting and memorizing their notes or rote recall using flash cards. As such, most students continue using these same techniques in first-year STEM courses1, such as physiology. Furthermore, first-year learners are often unsure how to change their study habits, even after receiving a disappointing midterm grade, as most post-secondary students report that they have not been taught how to study2. This leads to the reinforcement of surface learning, creating a larger knowledge gap as students progress into upper-year courses without having achieved deeper learning in pre-requisite courses. Although many universities offer learning strategy workshops, it has been shown that teaching students these skills within the context of their course work is the most effective3. We hypothesize that modelling deep learning strategies through a variety of weekly hands-on activities will positively influence students’ self-reported study strategies, leading to the incorporation of these active learning techniques in their personal study time. Our first-year physiology course is a pre-requisite for both Kinesiology students and those in the Foods and Nutrition program. Approximately 420 students are enrolled and includes those in science and non-science majors. We have 12 tutorial sections of approximately 40 learners each and 21 weeks of tutorials over the academic year. These tutorials serve as active learning sessions that apply lecture content from the week earlier. Each week, we have designed activities such as concept mapping, case studies, card sorting, problem set worksheets, and Play-Doh modelling, to name a few. Students complete a learning attitude survey after each activity, where quantitative and qualitative data is collected, and two test-your-knowledge exit questions on the content covered by that activity. Students experience each activity in both semester 1 and semester 2, although for different course material. We will present examples of activities used during tutorials as well as preliminary data, which is currently being collected, on the learning attitudes submitted by our students for these activities. We anticipate that as deeper learning strategies are modelled in tutorials, students will be more likely to adapt these techniques as they study. We also anticipate that repeated exposure to the same learning activity in semester 2 will positively influence their attitude toward the usefulness of that tool. Physiology educators have embraced a shift away from didactic lectures to expand the use of problem-based learning in recent years. Our study contributes concrete examples that any educator could implement in their classroom and the associated learning attitudes regarding each activity from a diverse group of students taking their first physiology course. References cited: 1Cook, A. and Leckey, L. (1999) Do expectations meet reality? A survey of changes in first-year student opinion. Journal of Further and Higher Education. 23: 157-171. 2Kornell, K. and Bjork, RA. (2007) The promise and perils of self-regulated learning. Psychonomic Bulletin & Review. 14: 219-224. 3Hattie, J., Biggs, J., and Purdie, N. (1996) Effects of learning skills interventions on student learning, a meta-analysis. The Journal of Experimental Education. 66: 99-136. No support or funding. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,006
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,013
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0020,001
Communication savante0,0050,003
Science ouverte0,0020,008
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

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 Opus0,044
Tête enseignante GPT0,399
Écart entre enseignants0,355 · 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 source (Gemma direct ou Codex distillé), 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
Publié2024
Routes d'admission1
Résumé présentoui

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