An evaluation of human movement studies curricula of Nigeria tertiary institutions
Bibliographic record
Abstract
Curricula of studies generally in most countries are dynamic, and continuous facets of education process depicting changes and needs of societies. Tertiary institutions as the upper echelon of education in Nigeria have experienced very little changes in their curricula since the ‘70s, the only notable change being the accreditation of academic programmes of the higher institutions initiated by the National Universities Commission (NUC) in the early ‘80s. This is also true of curricula of Physical and Health Education (PHE) offered by most Universities and Colleges of Education in the country. The stagnation in the PHE curricula does not reflect the numerous changes and transformations the country has experienced over the past three decades. If higher education should serve the needs of the people and the country, such needs must be reflected in the curricula of studies of our institutions. Except for a few universities, the focus of the PHE curricula in Nigeria has remained too dogmatic and uninspiring. Presently all over the world the focus has shifted from merely producing participants (e.g. athletes), teacher preparation, health education, to preventive health, health and wellness of individuals, prevention of major risk factors of life, etc. Hence the adoption of various nomenclatures such as Human Movement Studies, Biokinetics, Human Kinetics, Sport, Exercise and Nutritional Sciences, Kinesiology, Sport and Physical Rehabilitation Sciences, Recreation and Leisure Sciences, etc to emphasise the focus. The current trend of training in Human Movement Studies as practised in countries like USA, Australia, Britain, Canada, South Africa, etc gives room for programme diversification and specialisation and better jobs opportunities for students. This paper therefore analyses the present PHE curricula of Nigeria tertiary institutions, and the need for a change in curricula with a view to: (1) adopting more focused nomenclatures, (2) developing diversified programmes of study that cater for the current needs of the country and interests of students, (3) creating more areas of specialization, focus and application, and (4) creating better job opportunities for the students. The paper benchmarks current trends and practices in Human Movement Studies in both developed and developing countries of the world to justify the call for PHE curricula review. (Af. J. Physical, Health Education, Recreation and Dance: 2003 9(2): 277-292)
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".