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Record W4412775229 · doi:10.5539/elt.v18n8p76

Teachers’ Self-Efficacy with English Learners: Pre and Post a U. S. Statewide Initiative

2025· article· en· W4412775229 on OpenAlexvenueno aff
Janet Penner-Williams, Amirreza Karami

Bibliographic record

VenueEnglish Language Teaching · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySelf-efficacyMathematics educationPedagogyMedical educationSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to compare teachers’ self-reported efficacy in working with English Learners (ELs) before and after participating in state-wide professional development (PD) funded by the Arkansas Department of Education. A modified Teachers’ Sense of Efficacy Scale-short form (Tschannen-Moran & Hoy, 2001) which has shown reliability and validity was utilized. This survey measured self-efficacy in three subscales of teaching (a) Instructional Strategies for ELs, (b) Classroom Management for ELs, and (c) Student Engagement for ELs. Participants in this study included 214 inservice PK-12 teachers, coordinators, and school leaders who did not have an English as a Second Language (ESL) endorsement on their teaching license and were currently employed by a public school or district in Arkansas at that time. Statistically significant differences between presurveys and post surveys on the paired-samples t-tests for the three subscales had effect sizes ranging from small to large, with the largest in instructional strategies. Additionally, two analyses of variance were performed to find out whether there was a statistically significant difference in self-efficacy between educators in terms of their years of experience. Overall, the findings of the study suggest the job-embedded PD provided by ADE had a positive influence on educators’ self-efficacy in working with ELs at all levels of work experience.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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