Does Online Anatomy Course Delivery Improve Visuospatial Ability in Novice Learners?
Notice bibliographique
Résumé
Introduction The study of anatomy can be challenging for students, as complex anatomical and spatial relationships can be difficult to master, particularly in virtual learning environments. Having good visuospatial ability (VSA) can be important for students to have in order to help them understand complex three‐dimensional relationships. There is evidence that learning anatomy in‐person can improve an individual's VSA and that those with higher VSA perform better in anatomy courses. However, no research has explored what effect learning anatomy in a completely virtual environment has on VSA. Thus, the first aim of this study is to determine if there are changes in VSA with online anatomy instruction in novice learners over one semester, and second, to examine if those with higher VSA interact differently with online resources. For our first aim, we hypothesize there will be an overall improvement in VSA over the course of the semester. Our second aim will be analyzed at a later date. Methods First‐year undergraduate anatomy students at the University of Toronto were recruited for this study (n = 253). At the beginning of the semester in September 2021, before learning anatomical content, participants completed the redrawn Vandenberg and Kuse Mental Rotations Test (MRT‐A) which consisted of 24 questions. For the MRT‐A, participants were presented with a target figure and four stimulus figures, where two were a match to the target figure, but had been rotated. If both stimulus figures chosen were correct, one point was rewarded, for a total of 24 points and the full scores calculated out of 100 (24/24). In December 2021, another MRT‐A test will be administered, along with an online questionnaire that will ask students about what type of resources they used throughout the semester and how often they were used. In January 2022, focus groups will be conducted to gain a more in‐depth understanding into which learning tools students found most useful and why, along with how they were being used. Results Preliminary results indicate an initial mean score of 35.20 ± 17.54 with a median of 33.33. Students that scored below the median were categorized as low VSA (n = 112) and those who scored above were categorized as high VSA (n = 140). Additionally, the mean of students that identify as male had a mean of 40.55 ± 18.14 (high VSA = 83; low VSA = 39), those who identify as female had a mean of 30.03 ± 29.17 (high VSA = 55; low VSA = 70), and those who identify as non‐binary or chose “Prefer not to say” had a mean of 31.25 ± 17.14 (high VSA = 2; low VSA = 2). There was a significant difference between the mean score for males and females (p<0.001), but not for any other comparison. Conclusions Initial results appear to be in line with previous research suggesting that males have higher VSA than females. Future analyses will provide a more in‐depth evaluation of overall changes in VSA, gender VSA changes, and the type of resources students engaged with during their online anatomy course. This research may be used as a baseline for further studies exploring course delivery in‐person, mixed, and remote to help instructors create equitable learning environments for students with differing VSA. Additionally, this research will help inform the development of an anatomy learning toolkit that will be used to help leaners develop better spatial reasoning skills.
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 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».