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Record W2982360135 · doi:10.5539/elt.v12n11p85

Characteristics of EFL Curriculum in the Colombian Caribbean Coast: The Case of 12 State Schools

2019· article· en· W2982360135 on OpenAlexvenueno aff
Yuddy Pérez, Lourdes Rey Paba, Nayibe Rosado

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumConsistency (knowledge bases)PsychologyDescriptive statisticsExploratory researchEnglish as a foreign languageForeign languageMathematics educationDescriptive researchState (computer science)PedagogySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Research related to English as Foreign Language (EFL) curricula in Colombia is scant and mainly focuses on the analysis of policies and the challenges they pose to institutions. Consequently, there is a need for studies of EFL curricula in place in Colombia to serve as basis for evaluation and adjustment of current educational policies. This descriptive study follows a qualitative exploratory approach and addresses this gap by identifying the characteristics of the EFL curricula from twelve state schools. Data were reviewed and evaluated using document and comparative analysis techniques. Results reveal that all institutions have curricular documents but not all those requested by the educational authorities. Some of these documents show a lack of consistency in terms of the conceptual underpinnings as well as a misalignment with the national requirements or with contextual needs. While awareness of the importance of having strong English programs and the positive outlook shown by institutional stakeholders seem to be conditions for success, limitations related to allocation of resources, classroom conditions, and environments conducive to learning hinder the implementation of the EFL curriculum.

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)0.002
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 score0.031
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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