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Curriculum Development and Individual Social Responsibility in Nigeria: Exploring the Manitoba Art Education Curriculum Development Strategy

2020· book-chapter· en· W3092563508 on OpenAlexaboutno aff
Anuoluwa Maria Ajala

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumScrutinyCurriculum developmentHigher educationInclusion (mineral)Political scienceSocial changeCurriculum mappingPedagogySocial responsibilitySociologySocial sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

Abstract The importance of academic curriculum in higher education cannot be overemphasised. This explains the scrutiny to which the various models employed for the development of higher education programmes curriculum are subjected. In spite of the numerous scrutiny, higher education curriculum development is still infested with downsides. Solutions to these problems have been proffered by the strategy employed by Curriculum Development and Implementation Branch, Manitoba Department of Education in the development of the curriculum for Canadian Art Education in Manitoba. The inability to incorporate social responsibility into curriculum of higher education programmes has been a major setback in the actualisation of social responsibility in higher education in Nigeria. The tertiary institutions therefore need to look beyond just issuing degrees and diplomas but inculcate in their students the need to think beyond individual interest to societal interest. Based on this backdrop, this chapter explores the strategies employed by the Manitoba Department of Education in the curriculum development and how these strategies can be implemented in Nigeria for the inclusion of social responsibility into the curriculum of higher education. It focuses on identifying the variables integral to the construction of curricula of higher education programmes in the south-west geopolitical zone Nigeria.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.316
Teacher spread0.224 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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