Increasing ELL Parental Involvement and Engagement: Exploration of K-12 Administrators in a Rural State
Bibliographic record
Abstract
This study reports the findings from an exploration of K-12 administrators in a rural state about how they can more effectively engage and involve families of English language learners (ELLs). The guiding questions for this study are: (1) How does the role of administrators influence the engagement and involvement of ELL parents within K-12 education? (2) What can administrators do within their districts specific to their district in order to facilitate ELL parental engagement and involvement? Through an online survey and in-person interviews, the authors focus specifically on the perceived level of engagement of ELL families as it pertains to districts in general and a specific district. Furthermore, preconceived notions of expectations and language differences and the effectiveness of programs currently offered overall throughout the rural state are explored. Finally, the authors offer suggestions on how to better involve and engage ELLs and their families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".