Research-based Teacher Education for Multicultural Contexts
Bibliographic record
Abstract
Graduate programs in education face the challenge of preparing teachers and specialists in education to work with English Language Learners (ELLs). Programs must be culturally responsive, while at the same time respecting state and federal standards for scientifically based practice according to best evidence. The focus of the present study is a graduate program in education that sought to prepare graduate students to address the needs of ELL students. Among the articulated goals of the program grant were that teachers enrolled would be able to: (1) use effective English for Speakers of Other Languages and bilingual educational strategies and methods; (2) use findings from testing, assessment and research functionally; and (3) promote multilingualism, and, in a broader sense, respect and equitable treatment of the heritages of home languages. The extent to which graduates of the master’s program who were working as teachers and administrators at the time of the study were able to make culturally competent connections with ELL students and to establish a repertoire of scientific evidence, based on research findings that they could then use to support their teaching theory and practice, is discussed. Findings reflecting the responses of 57 graduates of the program were as follows: (a) the training provided by the master’s program was rated as more useful than the in-service provided by the state because its emphasis on research allowed graduates to judge the merits of proposed educational reforms and to clarify their own pedagogy; (b) the ability to cite research reports enabled graduates to be heard by colleagues and to depoliticize discussions regarding curricular reforms; (c) in developing their ‘communities of practice’, graduates made connections with others who had been trained in the use of scientific research in education. The study illustrates how a graduate education program focused on transformation and the encouragement of home language use can prepare teachers to work effectively in a political context of ‘evidence-based practice’.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".