What Makes a Good Anatomy E‐module? A Quantitative and Qualitative Evaluation of Occupational Therapy Introductory Anatomy E‐modules
Notice bibliographique
Résumé
Introduction The Master of Occupational Therapy (MScOT) program at Western University attracts students from diverse backgrounds, including those from non‐science fields. As anatomical competency is essential for effective patient management and safe practice, students in this Master’s program are required to complete training in anatomy. However, past student feedback had indicated that those with no prior experience in anatomy struggled with basic anatomical concepts, as limited time is allotted to foundational anatomical knowledge in the first year of the curriculum. Therefore, four introductory anatomy e‐modules were developed to prepare students for the anatomical components of the program. Our previous pilot study in 2020 had described the design of e‐modules and quantitative results indicated that completion of the introductory e‐modules allowed first‐year MScOT students with minimal prior anatomy experience to gain baseline knowledge and draw level with students who have more anatomical experience. The current study, conducted on the 2021 cohort of first‐year MScOT students, shifts the focus to evaluating how multimedia design elements in the e‐modules foster or hinder students’ anatomy learning experience. The collection of qualitative data in this study aims to complement the quantitative results from our previous findings to provide a holistic understanding of how learning design shapes module effectiveness. Methods This study consists of two components. The quantitative portion assesses the effectiveness of the introductory e‐modules through the comparison of first‐year MScOT students’ knowledge test scores before and after completion of the e‐modules. The qualitative portion analyzes responses in an evaluation survey to identify emerging themes regarding multimedia module design through thematic and content analysis. Further analysis will group knowledge test questions by their associated module to examine the alignment between actual and students’ perceived effectiveness of multimedia elements incorporated into e‐modules in enhancing learning outcomes. Results Preliminary results indicate significant improvements in performance in the post‐test, consistent with our findings in the previous study. In the evaluation survey, the majority of participants rated the interactive yoga studio section in Module 1 and recurrent learning checks as the most effective multimedia elements that fostered learning. Conversely, the rapid pace of the narration and the busyness of slides were most commonly listed as elements that hindered learning. Conclusion This study utilizes Mayer’s theory of multimedia learning in the development of e‐modules to identify multimedia design elements that students perceive to be beneficial for learning. Preliminary findings suggest the need to incorporate active learning elements in a virtual environment to enhance students’ anatomy learning experience. In addition, instructors should account for greater expectations from students regarding the quality of computer‐assisted technology compared to in‐person learning – such as optimal pacing and content – to reduce the extrinsic load and prevent hindrance of students’ learning.
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 | 0,003 | 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 | 0,001 |
| 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 ».