Evaluating the quality of medical multiple‐choice items created with automated processes
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
OBJECTIVES: Computerised assessment raises formidable challenges because it requires large numbers of test items. Automatic item generation (AIG) can help address this test development problem because it yields large numbers of new items both quickly and efficiently. To date, however, the quality of the items produced using a generative approach has not been evaluated. The purpose of this study was to determine whether automatic processes yield items that meet standards of quality that are appropriate for medical testing. Quality was evaluated firstly by subjecting items created using both AIG and traditional processes to rating by a four-member expert medical panel using indicators of multiple-choice item quality, and secondly by asking the panellists to identify which items were developed using AIG in a blind review. METHODS: Fifteen items from the domain of therapeutics were created in three different experimental test development conditions. The first 15 items were created by content specialists using traditional test development methods (Group 1 Traditional). The second 15 items were created by the same content specialists using AIG methods (Group 1 AIG). The third 15 items were created by a new group of content specialists using traditional methods (Group 2 Traditional). These 45 items were then evaluated for quality by a four-member panel of medical experts and were subsequently categorised as either Traditional or AIG items. RESULTS: Three outcomes were reported: (i) the items produced using traditional and AIG processes were comparable on seven of eight indicators of multiple-choice item quality; (ii) AIG items can be differentiated from Traditional items by the quality of their distractors, and (iii) the overall predictive accuracy of the four expert medical panellists was 42%. CONCLUSIONS: Items generated by AIG methods are, for the most part, equivalent to traditionally developed items from the perspective of expert medical reviewers. While the AIG method produced comparatively fewer plausible distractors than the traditional method, medical experts cannot consistently distinguish AIG items from traditionally developed items in a blind review.
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.
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 | 0,024 | 0,877 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 découle