Should assessments match modern teaching methods within physiology?
Notice bibliographique
Résumé
<b>Abstract Number: 226</b><b><br/></b>***********************************************<b/><b><b><br/></b></b><b>Background: </b>Traditionally, medical students were taught disciplines such as physiology and anatomy through the use of dissections or silicone models alongside two-dimensional textbook materials. As the volume of information required to learn in a modern-day medical course increase, and teaching shifts to multimodal delivery, educators are increasingly utilising technology-enhanced resources. Augmented and virtual reality have successfully been employed for learning and teaching within many medical and health sciences programs to provide engaging and interactive learning experiences. For disciplines such as physiology and anatomy, these technologies may disrupt the traditional modes of content delivery. However, the overall evidence-based benefits and effectiveness of these devices for student learning remain unclear. Determining the viability of these teaching tools is of importance as universities have been consistently increasing their use of technology to supplement learning within health sciences in recent years. We undertook a systematic review and meta-analysis to evaluate the impact of virtual reality or augmented reality on knowledge acquisition for students studying preclinical physiology and anatomy, and also investigated any impacts on assessment performance. <br/><b>Methods:</b> The protocol was submitted to Prospero and a literature search was undertaken in PubMed, Embase, Cochrane, ERIC, and other databases from January 1990 to November 2019. Inclusion criteria included randomised controlled trials assessing knowledge acquisition and learning in preclinical physiology and anatomy using virtual or augmented reality compared to traditional teaching methods. <br/><b>Results:</b> Of nine hundred and nineteen records, fifty-eight articles were reviewed in full text, with eight studies meeting full eligibility requirements. The studies included a total of six hundred and twenty-six participants, conducted in Australia, Germany, Canada, United States, and Turkey. Nearly all studies included followed a two-arm parallel randomised trial design, with one being a cluster randomised controlled trial and one a three-arm parallel trial. There were no significant differences in knowledge scores from combining the eight studies, with the pooled difference being a non-significant increase of 2.86% (95% CI [−2.85; 8.57]). Analysis was undertaken to compare results between the two groups, augmented and virtual reality, however the difference in knowledge scores was non-significant (p = NSD). <b><br/></b><b>Conclusions:</b> This systematic review has identified similar benefits of traditional teaching methods to virtual or augmented reality in physiology and anatomy education. However, although augmented and virtual reality can enhance the overall learning experience, methods of assessment also need to be introduced to properly ensure equity in any introduced learning tool. Overall, the evidence suggests that although test performance is not significantly enhanced with either mode, both augmented and virtual reality are viable alternatives to traditional methods of education in health sciences and medical courses.
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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».