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Record W4283165226 · doi:10.5430/wjel.v12n5p370

Exploring the Practice of Action Research among English Language Teachers in Omani Public Schools and Factors Affecting its Implementation

2022· article· en· W4283165226 on OpenAlexvenueno aff
Ahoud Al-Mamari, Abdo Mohammed Al-Mekhlafi, Fawzia Al Seyabi, Ehab Omara

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsWorkloadContext (archaeology)Action researchEnglish languageAction (physics)IncentivePoint (geometry)Affect (linguistics)Medical educationPedagogyPsychologyMathematics educationComputer scienceMedicineGeography

Abstract

fetched live from OpenAlex

The present study explored the extent to which action research is practiced by English language teachers and the factors that affect action research implementation in the Omani ELT context. Additionally, it sought to identify different solutions that could be implemented to strengthen action research practices from the point of view of English language teachers. Data was collected through a questionnaire that was distributed among 366 English language teachers, followed by semi-structured interviews with 8 English language teachers who were actively engaged in conducting action research. There was a low level of practice of action research among English language teachers in the Omani public schools. The study also showed that insufficient time, heavy workload, lack of colleagues’ assistance and support, and lack of motivation were the main factors that hindered action research practices in the Omani ELT context. Participants suggested that providing training, support and incentives as well as encouraging collaboration with experts and publications could strengthen the practice of action research in ELT.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.122
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.122
Threshold uncertainty score0.648

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1220.092
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.014
Scholarly communication0.0080.006
Open science0.0020.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.175
GPT teacher head0.465
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2022
Admission routes1
Has abstractyes

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