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Record W4386050261 · doi:10.5430/wjel.v13n7p473

Investigating the Process of Developing a Workplace English Digital Course for Oman Petroleum Academy and Industry

2023· article· en· W4386050261 on OpenAlexvenueno aff
Iman Al-Khalidi, Duhai Khalifa Duhai Al-Shukaili, Majdi Mohammed Said Ali Sulaiman, Omaima Al Hinai, Mariam Al-Sabah, Samaher Said Mohammed Al-Harrasi

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Needs analysisCourse (navigation)Petroleum industryDeveloping countryMedical educationComputer scienceKnowledge managementEngineering managementPsychologyMathematics educationEngineeringMedicine

Abstract

fetched live from OpenAlex

This study is part of a considerable project that the researchers endeavour to accomplish upon approaching the findings and developing a course design framework. The project aims to propose a training digital course offering English for the workplace (EWP) for Oman petroleum academy and industry. The study aims to explore the needs of Omani petroleum staff when developing the English for the workplace course. The study is shaped and guided by the approaches of ESP, learner needs, and e-learning. The data were collected via two methods, the survey questionnaire and semi-structured interviews. The data analysis method explored certain findings and themes that are identified in terms of linguistic and workplace needs/soft skills that must be considered when developing an effective and engaging course that suits the modern workplace setting and the digital age.

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)0.011
metaresearch head score (Gemma)0.021
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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