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Record W4292325247 · doi:10.11591/ijere.v11i3.22550

Perceptions of, and attitudes towards, English teaching and learning in Cameroon’s technical education

2022· article· en· W4292325247 on OpenAlexafffund
Innocent Mbouya Fassé, Alain Flaubert Takam

Bibliographic record

VenueInternational Journal of Evaluation and Research in Education (IJERE) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Lethbridge
FundersUniversity of Lethbridge
KeywordsRemedial educationInclusion (mineral)PerceptionPedagogyMathematics educationChristian ministryPsychologyTeaching methodSet (abstract data type)Political science

Abstract

fetched live from OpenAlex

<p><span lang="EN-US">This study examined the current practices, difficulties and impacts of English as second official language (ESOL) teaching and learning in secondary schools in Cameroon. It investigated the perceptions and attitudes of students, teachers and parents towards the teaching and learning of ESOL, including prevailing teaching and learning practices. This study stemmed from the observation that the exit profile of most students in technical secondary schools does not correspond to the official exit profile set out by the Ministry of Secondary Education (MINESEC). It was therefore necessary to survey students, parents, and teachers with the goal of identifying areas of concern and proposing remedial solutions. Responses of these key stakeholders selected in four education institutions (including two technical high schools and two general high schools) to questionnaires have provided data for the study. Such responses offered insights into the current situation in Cameroon’s ESOL, as well as into the possible utility of, and desire for, the development of ESOL courses aimed at students learning in technical schools. The inclusion and development of English for specific purposes (ESP) in Cameroon’s ESOL teaching and learning could help bring education stakeholders and policymakers closer to what they want to see from the country’s ESOL program.</span></p>

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.071
GPT teacher head0.446
Teacher spread0.375 · 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 designOther design
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

Citations0
Published2022
Admission routes2
Has abstractyes

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