Validating a blended teaching readiness instrument for primary/secondary preservice teachers
Notice bibliographique
Résumé
Abstract Blended learning is the fastest growing teaching modality in North America and much of the world. However, research and training in blended learning are far outpaced by its usage. To remedy this gap, we developed a competency framework and Blended Teaching Readiness Instrument (BTRI) to help teachers and researchers evaluate teacher readiness for blended environments. The purpose of this research is to show that the blended teaching readiness model and accompanying BTRI are reliable for use with teacher candidates both before and after going through a blended teaching course. This knowledge would allow researchers and practitioners to have greater confidence in using the BTRI for future growth curve modeling for the identified blended teaching competencies. To accomplish this, we collected pre‐ and post‐data from teacher candidates across multiple semesters who were studying in a blended teaching course. Using confirmatory factor analysis, we determined the pre‐class survey results fell within the range of the four fit statistics cutoffs (RMSEA = 0.045, CFI = 0.933, TLI = 0.929 and SRMR = 0.043). And, the post‐class survey results had good fit as well (RMSEA = 0.044, CFI = 0.911, TLI = 0.905 and SRMR = 0.051). We also showed that the factor loadings and communalities were statistically significant. By testing the factors in this way, we make a case for the survey to be a valid and reliable instrument in assessing blended teacher competency. Additionally, we tested the model for measurement invariance and found that we could reliably use the BTRI for pre‐post growth modeling. Practitioner Notes What is already known about this topic? Blended learning is the fastest growing teaching modality in Canada and the United States, and is expanding rapidly throughout the rest of the world. Teaching in blended learning settings requires distinct skills and dispositions specific to the modality. A blended‐teaching‐focused competency framework is a necessary element in any blended teacher preparation program. Though there have been attempts to make a blended teaching framework before, none of these exclusively focus on the distinct skills of blended teaching nor have they been validated. What this paper adds? Describes our free, publicly accessible competency framework that focuses exclusively on blended teaching Validates a concise Blended Teaching Readiness Instrument (BTRI) to go along with the framework. Confirms pre‐post measurement invariance for the BTRI which allows for use with pre‐post growth modeling. Implications for practice and policy The competency framework and validation are a theoretical contribution to the rapidly expanding field of blended learning research. With the valid BTRI instrument and framework, teachers can get feedback on their strengths and weaknesses in blended teaching and learn how to improve and help others.
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 | 0,011 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».