Creating Confident Preservice Teachers for EL Students in the Changing World
Bibliographic record
Abstract
English Learners (ELs) represent the most diverse group of students and a student population that has increased significantly in the United States. These students demand well-equipped teachers who have adequate preparation and pedagogical tools to meet their diverse needs. This research examined preservice teachers’ (N=77) perceptions of preparedness and efficacy beliefs from three different educator preparation programs using a mixed-method study that collected data from a 30-item survey as well as candidates’ narrative responses about preparation experiences for working with ELs. Findings included statistical differences in teachers’ perceptions of preparedness based on the preparation program they were enrolled in (e.g., bachelor’s or master’s) and whether teachers were receiving an ESL/ESOL certification as part of their initial preparation. Moreover, preservice teachers reported that ESL coursework, specific workshops that honed into ESL pedagogies, and field-placement opportunities to interact with EL students were influential in improving their abilities and confidence in the classroom. These findings suggest the continual need for teacher education programs to embed related ESL coursework as well as placing preservice teachers in clinical settings with EL students to influence effective pedagogies for the success of EL students in the classroom and beyond.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".