Learning Anatomy: Using the Blooming Anatomy Tool to determine how course delivery and duration affect the performance of anatomy students
Notice bibliographique
Résumé
During the fall/winter intake (FW), the University of Western Ontario offers a 3rd year systemic human anatomy course (ANATCELL 3319) in face to face (F2F) and online sections. F/W F2F students attend a weekly 1‐hour cadaveric lab where they view and manipulate prosections while FW online students attend a weekly interactive video conference lab session where their teaching assistant display PowerPoint slides, anatomy software and other materials. Both sections share the same multi‐choice questions (MCQ). During the summer intake, the course is only given in the online section with the labs given twice a week. Our previous study, relating to a different academic year, showed that FW F2F and FW online students performed equally well on their final grades. At that time, online students had access to Netter's 3D Interactive Anatomy to study laboratory materials. In this current study, students were not given access to anatomical software, however, their teaching assistant used 3D4MEDICAL for instruction purposes. This study compared the MCQ scores between the FW sections and the summer intake taking into account the cognitive level of the questions as determined by the Blooming Anatomy Tool (BAT) developed by Thompson & O'Loughlan (2015). The BAT rubric consists of knowledge, comprehension, application and analysis levels. During FW, F2F (n= 156) and online (n= 197) students had MCQ (n= 300) classified according to the BAT as follows: 149 knowledge, 120 comprehension, 21 application and 10 analysis questions. The scores in BAT levels were compared between the two sections. F2F students scored significantly higher than FW online student in knowledge, comprehension and application as well as the total MCQ (p≤ 0.001). The lack of difference in analysis could be due to the low MCQ number in that category. When comparing the scores across BAT levels within each section, application did not differ from comprehension in F2F but was lower for FW online students (p= 0.026) suggesting that F2F students are better at solving application MCQ. It should be noted that the performance of students in their prior years was the same for both the FW F2F and online sections which suggests that the improved performance by the F2F students is related to the manner in which the laboratory material is delivered. When the scores of F/W online (n= 197) and summer online (n=41) students in common MCQ (n=222 with 108 knowledge, 94 comprehension, 14 application and 6 analysis) were compared, FW students scored higher than summer students in knowledge (p< 0.001) and comprehension (p=0.001) as well as the total MCQ (p< 0.001). It should be noted that the incoming grades were higher for FW than summer online students (p< 0.05), which may account for the difference outlined above. In summary, the results suggest that the performance of FW online students declined year over year when they were no longer provided with personal versions of the software used online (Netters 3D Interactive Anatomy). In addition, we found that learning anatomy F2F using prosections may increase the ability to solve application questions when compared to FW online labs. 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 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 | 0,004 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».