The Efficacy of Florida’s Approach to In-Service English Speakers of Other Languages Teacher Training Programs
Bibliographic record
Abstract
Much of how Florida and other states across the country justify the practice of mainstreaming English language learners into regular content classrooms rests on the premise that with the guidance of state officials, local school districts adequately train content teachers to work with English language learners. Yet little to no research exists that can help identify and analyze the overall efficacy of these programs. Consequently, this study has attempted to determine whether district training sessions in Florida are sufficiently covering the state-mandated content areas that teachers are required to learn and to what extent in-service teachers agree or disagree that they received the appropriate amount of instruction that would prepare them to instruct English language learners. Training sessions in three large Florida school districts with high proportions of English language learners were studied using a mixed-methods approach that gathered quantitative and qualitative data from observations, surveys and in-depth interviews. Among other things, the findings revealed a pattern of districts overemphasizing cross-cultural awareness issues to the detriment of other critical areas teachers need to know such as methods and curriculum. In addition, there was a general consensus on the part of participants that the trainings lacked specificity and were both impractical and redundant. A number of specific recommendations are offered such as ways to modify the focus of the curriculum, provide incentives to teachers, and create more accountability and oversight of the training sessions themselves. Policymakers are strongly urged to prioritize these types of programs by providing training sessions with more resources and attaching to them a larger sense of importance.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".