Adopting Instructional Strategies for English Language Learners in Elementary Classrooms
Bibliographic record
Abstract
In an Eastern U.S. school district, little is understood about how elementary general education teachers apply instructional strategies for English Language Learners (ELLs) in the classroom and which strategies they perceive support academic achievement. The purpose of this basic qualitative study was to explore elementary general education teachers’ reported application of ELL instructional strategies and their perceptions of how those strategies support ELL academic achievement. The study’s conceptual framework consisted of Vygotsky’s sociocultural theory, which infers that learning is a social process guided by interactions with one’s environment, people, and culture. Also framing this study was Krashan’s second language acquisition theory (Long, 1983), which infers that language is attained though one’s strong desire to interact with the world around them. Two research questions were used to investigate the reported ELL instructional strategies used by teachers and how teachers perceive those strategies support ELLs’ achievement. Semistructured interviews were conducted with 11 elementary general education teachers. Volunteers were recruited from schools having ELL populations of 30% or more. Interview data were analyzed by using open and a priori codes and thematic analysis. The findings indicated that participants used familiar instructional strategies and consistently applied them for the whole class. Additionally, participants perceived ELLs’ academic confidence and connecting concepts with their primary language as important to academic achievement. This study contributes to positive social change through a deeper understanding of the ELL instructional strategies that may benefit elementary teachers and stakeholders.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".