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Record W2897199496 · doi:10.5539/elt.v11n11p83

Is Communicative Language Teaching Being Tested Communicatively? An Analysis of English Tests in Oman

2018· article· en· W2897199496 on OpenAlexvenueno aff
Maya Rashed Hamdan Al Mamari, Abdo Mohamed Al-Mekhlafi, Thuwayba Ahmed Al-Barwani

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyVocabularyLanguage assessmentTest (biology)ChecklistCompetence (human resources)Mathematics educationTest of English as a Foreign LanguageLinguisticsContext (archaeology)Communicative competenceCommunicative language teachingEnglish languageDescriptive statisticsLanguage educationPedagogySocial psychologyStatistics

Abstract

fetched live from OpenAlex

This paper investigates the degree of communicativeness of English final tests in Oman. It is a descriptive, content-based analysis of Five Final tests from 2011 to 2016. A framework and a checklist were developed for purposes of analysis. The framework was based on Bachman and Palmer's model of language ability (2010) and grade 10 English test specifications. Research findings reveal: (i) A discrepancy between test specifications of English final tests and the actual content of the tests (ii) That communicative competence was not fully addressed in the English final tests (iii) That the input used in English final tests lacks authenticity (iv) The majority of the test components used suitable language in a limited context with limited instructions given for some components. There was also lack of constructed responses (v) There was no integration between two skills or elements on the test, except for a random integration between vocabulary and reading.

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)0.004
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.303
Teacher spread0.281 · 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 designObservational
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

Citations6
Published2018
Admission routes1
Has abstractyes

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