Development and Implementation of an Online OSPE Test Bank Graded by Artificial Intelligence
Notice bibliographique
Résumé
The development of online anatomy education has rapidly accelerated during the COVID‐19 pandemic. This shift to the online world has mainly been focused on the delivery of content, while testing anatomical knowledge has proven to be more challenging, particularly for objective structured practical exams (OSPEs, also known as “spot tests” or “practical exams”). Online resources for OSPEs are uncommon compared to the extensive banks of multiple‐choice questions available. Another issue is that whether virtual or in‐person, grading OSPEs is challenging and time consuming. Recent research in our laboratory has suggested that machine learning algorithms, using decision trees, can be trained to mark OSPEs with a >95% accuracy. Building on these findings, the goal of this project is to create a virtual OSPE bank, train the AI to grade OSPEs, and develop an application with automated AI grading to act as a resource for students studying anatomy and physiology. Currently, we have written over 120 OSPE question sets using images from the Bassett Collection, the UBC Neuroanatomy collection, and images developed at the Education Program in Anatomy at McMaster University. The questions and answers were initially developed by senior undergraduate students, with coaching from faculty and staff familiar with OSPE generation acting as experts. The questions were then collectively reviewed by the students before undergoing two independent reviews by the experts. After revision, a final blind review of the questions was undertaken by a third expert to ensure validity and accuracy. These questions are being made available on the undergraduate anatomy and physiology course learning management system (LMS), Avenue to Learn. On this LMS, students will be able to use the questions for OSPE practice and answers will be available in the traditional manner. The answers given by the students and graded by the faculty will be collected and analyzed to determine the difficulty and discrimination of the questions. The data and analyses will be used to refine additional OSPEs, as well as refine the AI marking tool for the virtual OSPE application. Our hypothesis is that by providing instant and accurate feedback on valid and accurate questions, course evaluations and performance on OSPEs will improve. The research group is actively searching for collaborators willing to generate and review additional OSPE questions to expand the question bank and improve the AI marking tool.
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 | 0,000 | 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 | 0,000 |
| 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 ».