Developing a Theory of Change: How Are Teacher Educators Preparing Pre‐ and In‐Service Teachers of Multilingual Learners?
Bibliographic record
Abstract
ABSTRACT This research brief describes a collaborative and culturally responsive evaluation process employed in developing an evolving Theory of Change (ToC) for the Department of Education NPD grant, the English Learners' Educational Excellence Capitol Teacher Training Project (Project ELEECT). The brief outlines the framework for understanding how change is anticipated among pre‐ and in‐service English Second Language (ESL) teachers of multilingual learners (MLs) engaged with culturally sustaining ESL pedagogies. We document the various phases involved in developing data collection instruments over the first 3 years of the grant, including the use of mixed method approaches such as pre‐ and post‐surveys and participant interviews. These instruments were carefully aligned with the ToC to provide a basis for evaluating the implementation and impact of the program. The data collection and analysis processes described in this brief were integral to refining the ToC framework, which in turn guided the development of the evaluation instruments. The evolving ToC served as a foundational tool to connect program activities and outputs with intended outcomes, thereby supporting a structured evaluation of the program's implementation and its expected impact on teacher preparation practices.
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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 | 0.054 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".