The Name Jar Project: Supporting Preservice Teachers in Working with English Language Learners
Bibliographic record
Abstract
Classrooms are becoming more linguistically and culturally diverse and many educators are feeling unprepared to meet the varied needs of English language learners (ELLs). Through a larger design-based research doctoral study, I collaborated with 11 preservice teachers and 28 ELLs in Grades 2 and 3 to design and implement a literacy intervention that focused on cultivating literacy engagement to foster English language development. This paper documents the positive impact the implementation of the literacy intervention, also known as the Name Jar Project, had on supporting the preservice teachers’ emerging practice. Analysis of focus group data, preservice teachers’ written reflections, and field notes revealed that (a) the preservice teachers, through their informal learning experiences, were able to empathize with the ELLs’ strengths and challenges of learning English; (b) the service learning model provided a safe learning environment for preservice teachers to gain practical experience working with ELLs; and (c) through the research design, preservice teachers connected practice and theory to inform their future teaching experiences.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".