Curriculum Development and Individual Social Responsibility in Nigeria: Exploring the Manitoba Art Education Curriculum Development Strategy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".