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Record W3010311123 · doi:10.5430/jct.v9n1p86

Teacher Education in Ghana: Policies and Practices

2020· article· en· W3010311123 on OpenAlexvenueno aff
Isaac Buabeng, Forster D. Ntow, Charles Deodat Otami

Bibliographic record

VenueJournal of Curriculum and Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmRestructuringTeacher educationAffect (linguistics)Teacher preparationProfessional developmentPolitical sciencePedagogyEconomic growthSociologyPsychologyEconomics

Abstract

fetched live from OpenAlex

This article focuses on teacher education in Ghana. It examines a number of reforms involving curricular changes and restructuring of teacher education institutions tasked with the responsibility of preparing teachers for the basic school level. The article highlights the structure and changes in Ghana’s teacher development policies and practices following the adoption of a new programme which took effect in 2018 with the intake of the first batch of 4-year degree students in the country’s Colleges of Education. We envisage that improved teacher qualification and a conscious effort to link theory to practice will result in improved teacher knowledge and skills required for a professional teacher. Despite this stated enthusiasm, a number of contextual issues which could negatively affect the intended gains from this most current reforms have been discussed. We end with a call on policy makers to address the contextual issues highlighted in this paper and also a need for continuity in teacher education policies in Ghana considering the numerous politically-related reforms.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.035
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.376
Teacher spread0.348 · 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

Citations53
Published2020
Admission routes1
Has abstractyes

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