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Record W2942116210 · doi:10.5539/jedp.v9n1p69

Cultivation of Polytechnic-Industry Linkage for Development and Delivery of Curriculum for Technical Education: A Case Study of The Federal Polytechnic, Ilaro

2019· article· en· W2942116210 on OpenAlexvenueno aff
Raheem Adisa Oloyo

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumGeneral partnershipLinkage (software)Vocational educationReputationBusinessEngineering managementEngineeringEconomic growthEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

The implication of the unsuitability of young Nigerian graduates for available jobs in the industry raises the question as to the appropriateness of the training received while in school. It has exacerbated the unemployment problem in the Country, and it is worrisome. Of a necessity in curriculum development and delivery, therefore is the adoption of an approach that takes cognizance of the job function of the graduates in the industry and/or workplace and the skills required to perform on the job. In other words, the curriculum must target the job market demand and needs. This paper reports on the development of a demand-led curriculum in National Diploma Cement Engineering Technology through the partnership of the Federal Polytechnic, Ilaro with Cement Industry, Cement Training Institute of Nigeria, Manufacturers Association of Nigeria, and the National Board for Technical Education. The paper concludes that the emerging graduates from the implementation of the curriculum would have acquired the appropriate skills for the job, and would be acceptable and fit to perform effectively in the industry. Besides, opportunities for earning industry research income and reputation through the provision of research support to the industry is an added benefit derivable from the linkage.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.458

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 head0.017
GPT teacher head0.300
Teacher spread0.283 · 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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