Assessing the multilevel validity of program-level inferences based on aggregate student perceptions about their general learning
Notice bibliographique
Résumé
Aggregate survey results are commonly used by universities in Canada to compare effective educational practices across program majors within a university and between equivalent majors across campuses. Despite this recurrent practice, many researchers neglect to examine the multilevel validity of inferences made from program-level responses. This study illustrates the importance of determining the multilevel validity of program-level inferences prior to making conclusions based on survey data. Survey responses regarding student perceptions about their general learning outcomes and ratings about the learning environment were collected from the National Survey of Student Engagement (NSSE) and the Undergraduate Experience Survey (UES). The analytic procedures used in this study included two-level exploratory multilevel factor analyses (MFA) and three statistical approaches to determine the appropriateness of aggregation: analysis of variance (ANOVA), the within and between analysis (WABA), and the unconditional multilevel model. Multilevel regression models were applied to survey data to examine the relationships of program-level characteristics with perceived student learning outcomes. The results led to four conclusions regarding the use of student survey results aggregated to the program level. First, results from the MFA revealed that the multilevel structure of items regarding perceived learning were consistent across the student and program levels for most samples, but the multilevel structure of items regarding the learning environment was not supported at the program level. Second, results from the ANOVA and unconditional multilevel models indicated that aggregation to the program level for perceived learning was statistically appropriate for three out of the four study samples; however, WABA results indicated that aggregation to a level lower than the program major was more suitable. Aggregation to the program level was not supported for any the learning environment scales across all three procedures. Third, aggregation was variable dependent as demonstrated by lower levels of within-program agreement on ratings of the learning environment, but larger levels of agreement with perceived learning outcomes. Finally, student-level perceptions about learning were partially influenced by student- and program-level characteristics; however, program means were not highly reliable and results did not support making program comparisons. Implications for educational research and recommendations for further research were discussed.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,114 | 0,343 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».