Studying Histology in 3D: Development and Evaluation of an Interactive Virtual Histology Learning Tool using a 3D Model of the Renal Corpuscle
Notice bibliographique
Résumé
Histology education relies on two‐dimensional (2D) histological sections where students are required to tacitly translate their 2D knowledge into a three‐dimensional (3D) understanding of complex structural‐functional relationships at the microscopic level. While in the field of Gross Anatomy, virtual 3D models address this cognitive hurdle, it is not known whether virtual 3D tools are effective in Histology education. The objective of our study was to develop an interactive virtual 3D Histology learning tool, and determine whether interaction with the tool results in improved learning outcomes in third‐year medical sciences students at Western University, as well as evaluate the relationship between spatial ability and students' histology knowledge. We used a virtual 3D model of the renal corpuscle, previously generated (Amira 5.2) from serial semi‐thin histological sections, to develop a virtual interactive tool that allows superimposing the 2D sections within the context of the 3D model. The model, and raw data sections in three orthogonal planes, were incorporated into Unity software to generate the learning tool. The human‐computer interface was designed to allow for various levels of interactivity. Among other capabilities these include: free rotation, zooming, choices of visualization of highlighted parts, scrolling through serial sections with or without the integrated model, textual and auditory explanation of highlighted components. To evaluate the efficacy of the tool, and spatial ability, students (n=156) participated in a two‐session study. During the first session participants completed a pre‐test, and a spatial ability test. These scores were used to generate three balanced groups: a 3D interactive group (access to the 3D tool), a 2D interactive group (access to virtual 2D slides), and a control group (access to static images). During session two, students were given access to one of the three available learning tools, followed by a post‐test, and a questionnaire to determine performance, student attitudes and perceptions towards the different learning modalities. Preliminary results demonstrate a significant positive correlation between histology knowledge and spatial ability (r=0.32, P<0.01). Completion of session two will determine any difference between pre‐ and post‐test scores in the 3D interactive group as compared to the control groups, and reveal any correlation to spatial ability. Virtual 3D learning tools for Histology may be effective at improving test performance, and fostering student engagement. These tools could therefore alter the way in which we teach and learn the subject, opening the door for future computer‐based technology to enhance Histology education. Furthermore, spatial ability may be a determinant factor of students' success in Histology that should be considered in curriculum design. This abstract is from the Experimental Biology 2018 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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».