Validating a blended teaching readiness instrument for primary/secondary preservice teachers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".