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Record W2108834597 · doi:10.5539/elt.v7n5p89

What Do Teachers Need to Support English Learners?

2014· article· en· W2108834597 on OpenAlexvenueno aff
Marjorie N. Gómez, Nagnon Diarrassouba

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCultural diversityPerceptionDiversity (politics)Mathematics educationPedagogyCultural competenceTeacher preparationQualitative researchTeaching methodTeacher educationSociology

Abstract

fetched live from OpenAlex

This study explored K-8 teachers’ perceptions of their preparation and the challenges they encountered in delivering instruction to culturally and linguistically diverse learners. Using a mixed method research design, data were collected through a web-based survey from teachers in the state of Michigan. Researchers used chi-square tests to investigate the relationship between teachers’ preparation and their knowledge of their diverse learners’ learning needs. Qualitative comments were examined, organized, and summarized to illustrate key themes in each question under study. Findings revealed that teachers’ perceptions of their preparation to teach linguistically and culturally diverse students were correlated with cultural diversity or lack of cultural diversity in their classrooms. Whereas teachers stated they felt prepared to teach heterogeneous classes, they encountered challenges in delivering instruction to English learners. In addition, teachers stated that cultural awareness training did not adequately prepare them to integrate cultural elements in their daily instructional practices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.014
GPT teacher head0.251
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2014
Admission routes1
Has abstractyes

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