‘What would my classmates say?’ An international study of the prediction‐based method of course evaluation
Notice bibliographique
Résumé
OBJECTIVES: Traditional student feedback questionnaires are imperfect course evaluation tools, largely because they generate low response rates and are susceptible to response bias. Preliminary research suggests that prediction-based methods of course evaluation - in which students estimate their peers' opinions rather than provide their own personal opinions - require significantly fewer respondents to achieve comparable results and are less subject to biasing influences. This international study seeks further support for the validity of these findings by investigating: (i) the performance of the prediction-based method, and (ii) its potential for bias. METHODS: Participants (210 Year 1 undergraduate medical students at McGill University, Montreal, Quebec, Canada, and 371 Year 1 and 385 Year 3 undergraduate medical students at the University Medical Center Groningen [UMCG], University of Groningen, Groningen, the Netherlands) were randomly assigned to complete course evaluations using either the prediction-based or the traditional opinion-based method. The numbers of respondents required to achieve stable outcomes were determined using an iterative process. Differences between the methods regarding the number of respondents required were analysed using t-tests. Differences in evaluation outcomes between the methods and between groups of students stratified by four potentially biasing variables (gender, estimated general level of achievement, expected test result, satisfaction after examination completion) were analysed using multivariate analysis of variance (manova). RESULTS: Overall response rates in the three student cohorts ranged from 70% to 94%. The prediction-based method required significantly fewer respondents than the opinion-based method (averages of 26-28 and 67-79 respondents, respectively) across all samples (p < 0.001), whereas the outcomes achieved were fairly similar. Bias was found in four of 12 opinion-based condition comparisons (three sites, four variables), and in only one comparison in the prediction-based condition. CONCLUSIONS: Our study supports previous findings that prediction-based methods require significantly fewer respondents to achieve results comparable with those obtained through traditional course evaluation methods. Moreover, our findings support the hypothesis that prediction-based responses are less subject to bias than traditional opinion-based responses. These findings lend credence to prediction-based as an accurate and efficient method of course evaluation.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 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 ».