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Record W4407355356 · doi:10.56279/ped.v42.suppl.i.1

Enhancing English language proficiency in Ghanaian colleges of education: exploring attitudes, challenges, and pedagogical perspectives from tutors and trainees

2024· article· en· W4407355356 on OpenAlexfundno aff
Gifty Edna Anani, Ernest Nyamekye

Bibliographic record

VenuePapers in Education and Development · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersMcGill University
KeywordsEnglish languagePedagogyLanguage proficiencyMathematics educationSociologyPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

This study examines factors inhibiting the quality of English language teaching and learning in Ghanaian colleges of education. Using a cross-sectional descriptive survey design, the study employed a mixed methods research approach, involving questionnaires with 128 teacher trainees from four colleges in the Eastern and Greater Accra zones and telephone interviews with 12 English language tutors. The findings reveal that while some teacher trainees have a positive attitude towards the English language course, they struggle with English concepts due to an overloaded curriculum content, limited instructional time, unfamiliar vocabulary, and ineffective teaching methods. Tutors also highlighted students’ poor foundational knowledge, a shortage of experienced educators, and inadequate professional development opportunities. The study calls for a balanced integration of essentialist and progressivist theories to enhance foundational knowledge, interactive learning, and effective teaching methodologies, contributing to improved English language education in Ghana.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.233
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.073
GPT teacher head0.307
Teacher spread0.233 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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