The Importance of Guidance: Teaching Histology with an Interactive Virtual 3D Tool of the Renal Corpuscle
Notice bibliographique
Résumé
Background Histology education classically relies on two‐dimensional (2D) sections where students are challenged to tacitly translate their 2D observations into a three‐dimensional (3D) understanding of complex structure‐functional relationships at the microscopic level. To overcome this cognitive hurdle, we previously generated a virtual 3D model of the renal corpuscle (RC) from serial histological sections and developed an interactive virtual 3D Histology learning tool (VHLT). The tool enables users to explore, listen, and read descriptions about a serially sectioned and digitally segmented RC. In addition, 2D serial ultramicrotome sections are superimposed over the 3D model and viewable in 3 orthogonal planes. This highly interactive interface allows users to freely rotate, zoom, and modify it by highlighting histological features, scroll through serial sections with or without the integrated model. Previously, the VHLT demonstrated improved student test results with low prior histology knowledge and those with low spatial ability. The current study explores how metacognitive guidance and learning preference affects student success. Specifically, we hypothesized students using the VHLT with guided instruction would perform better on post‐tests than unguided students using the VHLT and better than a control group using a traditional laboratory approach (2D Virtual Slide Box). All groups followed the same learning objectives. In addition, perceived student learning preference was considered in relation to the type of learning tool used. Participants (n62, 42F, 20M) in a large 3rd‐year introductory Histology course were categorized into learning preferences using the VARK model (visual, aural, read/write, kinesthetic, or multi‐modal) and completed a pre‐knowledge test of the histology of the RC. Using a VARK‐balanced allocation, learners were assigned to 2 experimental groups: guided instruction (GI), free exploration with the VHLT (UGI), and a control group (C). Average pre‐test histology scores across the 3 groups were 8.7±3.9, 7.4±2.8 and 7.8±2.7 for GI, UGI, and C groups respectively, with scores ranging from 3/30 to 15/30. During use of the assigned tools, investigator field notes and time on task were collected. The learning exercises took place in a laboratory environment where 3–10 students worked at computers. One week later, learners completed a post‐knowledge test that evaluated learning outcomes by comparing the change in test scores between pre‐ and post‐test. A questionnaire sampled learner preference with respect to their level of guidance and learning tool. Preliminary post‐knowledge test scores showed significant increases compared to pre‐test scores (14.8±3.7 for GI, and 13±5.4 for UGI, with scores ranging from 6/30 to 24/30). We predict that our data will reinforce the importance of metacognitive teaching approaches when using novel pedagogic tools. We also predict that guidance removes any potential differences that may appear in unguided uses where student learning preference may become a factor. This study will highlight how current and novel learning tools in histology education can be pedagogically potentiated through metacognitive adjustments in the organization of the undergraduate curriculum. 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 machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».