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Record W2338476366 · doi:10.5430/wje.v6n2p50

Language Models and the Teaching of English Language to Secondary School Students in Cameroon

2016· article· en· W2338476366 on OpenAlexvenueno aff
Njwe Amah Eyovi Ntongieh

Bibliographic record

VenueWorld Journal of Education · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsEnglish languageCompetence (human resources)Mathematics educationLanguage proficiencyLinguistic competencePsychologyPedagogyLanguage educationLanguage assessmentSociologyLinguistics

Abstract

fetched live from OpenAlex

This paper investigates Language models with an emphasis on an appraisal of the Competence Based LanguageTeaching Model (CBLT) employed in the teaching and learning of English language in Cameroon. Researchendeavours at various levels combined with cumulative deficiencies experienced over the years have propellededucational policy makers to carry out various changes in the models emphasised in the teaching of language. Suchchanges have been undertaken in view of improving proficiency in communication as well as performance in variousexaminations. This is especially apt during this era when there is a dire need and great aspirations towards evolvingCameroon into an emergent nation by 2035. Findings derived from different educational stakeholders, includingpedagogic inspectors, as well as school administrators, teachers and students from secondary schools located in theNorth West and South West regions of the Republic of Cameroon have been used in this investigation.Questionnaires, observations and interviews were employed to elucidate the information analysed in this study.

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 score0.129
Threshold uncertainty score0.209

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 head0.013
GPT teacher head0.291
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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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