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Record W2996907824 · doi:10.5430/jct.v9n1p1

Relevance of the Senior High School Curriculum in Ghana in Relation to Contextual Reality of the World of Work

2019· article· en· W2996907824 on OpenAlexvenueno aff
Okrah Abraham Kwadwo, Ernest Ampadu, Rita Yeboah

Bibliographic record

VenueJournal of Curriculum and Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumNonprobability samplingRelevance (law)PsychologyMathematics educationWork (physics)Soft skillsUnemploymentPedagogySociologySocial psychologyEngineeringPolitical sciencePopulation

Abstract

fetched live from OpenAlex

The mass unemployment of the youth toady has mostly been attributed to the irrelevance of the school curriculum. However, the skills in the curriculum have not been subjected to critical analysis to empirically prove their relevance or otherwise. The purpose of the study was therefore to identify the skills embedded in the curriculum, those skills the learners have acquired and those that employers usually demand of employees by relating them to empirical findings of the skills employers in general demand of employees. A conceptual content analysis was used to determine the skills embedded in the curriculum. Purposive sampling procedure was used to select twenty-one students and fourteen key informants for an interview. The data from the interview were sorted out into themes and coded through the use of NVivo 8 to help in the counting of frequencies of each skill. It was found out that the senior high school curriculum, though was generally rated as relevant, the skills with the highest frequencies in the curriculum focused on attitudes and values while those required by employers focused on the application of knowledge. On the basis of these findings, it can be concluded that the curriculum is relevant in instilling values into the students but it is not relevant in the application of knowledge that employers usually demand of employees at the work environment.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.018
GPT teacher head0.345
Teacher spread0.327 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

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