The Reality of Using Virtual Reality: Understanding How Undergraduate Students use a Virtual Bell Ringer App to Study Anatomy
Notice bibliographique
Résumé
Introduction With the increasing number of online resources for anatomical education available to students, understanding why students use one resource over another is crucial for resource design. The benefit of using stereoscopic images in anatomical education has recently been demonstrated (Remmele et al, 2018; Cui et al, 2017). However, few resources utilize stereopsis in depicting anatomical dissections. Using images from the Stereoscopic Atlas of Human Anatomy (D.L. Bassett & W.B. Gruber, Stanford University, 1962), we developed a smartphone application that uses the Google cardboard platform to visualize stereoscopic images in an inexpensive and accessible manner. The app was implemented in a second year undergraduate anatomy and physiology course with 975 students. The app was designed to incorporate virtual pins in the 3D space that enabled it to be used as a self‐evaluated Objective Structured Practical Exam (OSPE) called the Virtual Reality Bell Ringer (VRBR). Questions and answers accompanying the images were provided on the online course management system. This allowed the use of the app to be correlated with course evaluation performance. The PURPOSE of this study was to track use of the app by students, get feedback to develop better online resources, and correlate the app use with final scores. Methods 67 OSPE practice questions using stereo pairs from the Bassett collection were selected, 30 of which pertained to the first semester: 5 questions for each of 6 bi‐weekly labs. Questions related to each sets of images were available throughout the semester and students had unlimited attempts to successfully answer the questions. Upon submitting their response, students would see their answer, the correct answer, and the rationale behind the answer. Student participation and correlation between their VRBR score and multiple choice (MCQ) midterm exam were evaluated. Results By mid‐semester, 89 of 975 students attempted the VRBR practice questions. Of those who participated in the VRBR quiz, 93% achieved above average scores on their MCQ midterm exam. Correlation of midterm scores with successful VRBR question responses for the lowest, middle, and highest third percentile‐scoring students resulted in Pearson correlation coefficients (r) of 0.39, 0.5, and 0.78, respectively. This indicates that there is a good correlation between success on the VRBR practice questions and success on different types of course evaluations. Although 91% of students did not use the app to prepare for the midterm MCQ exam, a qualitative mid‐semester survey of app use revealed that 51% planned to complete the VRBR questions prior to the final OSPE exam. Only 13% of students reported that they feel that the app would not prepare them for the final exam. Conclusion Despite less than 10% of students using the app at mid‐semester, a large proportion planned to use the app to prepare for the final exam. There was a correlation between success on the midterm MCQ and VRBR quizzes, however, this is probably correlative rather than causative. Evaluation of VRBR app use on the outcome of the final OSPE exam will occur at the end of the first and second semesters. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0,002 | 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 | 0,001 | 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 ».