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Record W3086937065 · doi:10.5539/jel.v9n5p184

Reality of Linguistic Competencies of Pre-Service and In-Service Female Physical Education Teachers in the Sultanate of Oman

2020· article· en· W3086937065 on OpenAlexvenueno aff
Fatma Alkaaf, Durayra AlMaqbali, Yousra AL-Sinani

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical educationService (business)PsychologyMedical educationPopulationMathematics educationPedagogySociologyMedicineBusiness

Abstract

fetched live from OpenAlex

This study aims to identify the acquisition of linguistic competencies by pre-service physical education teachers at the College of Education, Sultan Qaboos University, and in-service physical education teachers in Muscat and Dhofar Governorates in Oman, as well as any statistical differences between these two groups. The population of the study consisted of 30 pre-service teachers and 28 in-service teachers. The observation card instrument was used to collect data. The results showed that the acquisition level of the linguistic competencies of pre-service physical education teachers was medium. However, the acquisition level of the linguistic competencies of in-service physical education teachers was high. Furthermore, there were significant differences between the acquisition level of linguistic competencies in pre-service and in-service teachers in favor of in-service teachers. Based on these findings, we recommend developing these competencies in pre-service teachers during the teacher education program by focusing particularly on planning, implementation, and assessment.

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)0.001
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 score0.144
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.037
GPT teacher head0.297
Teacher spread0.260 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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