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Record W2806294264 · doi:10.5539/ies.v11n6p73

EFL Syllabus Design: Challenges of Implementation in Burkina Faso

2018· article· en· W2806294264 on OpenAlexvenueno aff
Esther Somé-Guiébré

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusCurriculumCommunicative competenceCompetence (human resources)StakeholderPedagogyCommunicative language teachingArgument (complex analysis)Foreign languageSociologyMathematics educationPolitical sciencePsychologyLanguage educationPublic relationsMedicine

Abstract

fetched live from OpenAlex

The widespread use of English in the social, political, economic, and international business spheres compels non-English speaking countries to revise their English language curricula to meet the needs of the global economy. In Burkina Faso, educational policy makers have revised the English as a foreign language (EFL) syllabi from middle to high school with the expectation of helping students achieve communicative competence. However, the delayed implementation of these new syllabi unveils a discomfort from the perspective of both teachers and teacher supervisors. This paper provides a critique of the syllabi of quatrième (4ème) – the US equivalent of 8th grades. It draws from document analysis and stakeholder interviews to highlight the discrepancies between the theory of the vision and the reality of the practice and assess the extent to which the syllabus promotes or hinders communicative competence. The overreaching argument is that despite tremendous efforts invested in the conception of syllabi, these tools hardly help implement communicative language teaching (CLT) in their classrooms.

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)0.047
metaresearch head score (Gemma)0.089
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.089
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.002
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.135
GPT teacher head0.413
Teacher spread0.278 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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