An Evaluation of the Implementation Synergy between NCE-Integrated Science and the 9-Year Basic Science Curricula in Nigeria
Bibliographic record
Abstract
The Curriculum of the Nigeria Certificate in Education (NCE) has implications for the Curriculum of the 9-yearBasic Education (BEC) programme in Nigeria. Hence the National Commission for Colleges of Education (NCCE)came up with a vision of producing well motivated teachers with high professional integrity and competence. Boththe in-service and pre-service teachers need to be conversant with the content of the 9– year Basic EducationCurriculum (BEC). Are there implementation synergies in the topics, laboratory exercises and nature of assessmentin the NCE integrated Science and the 9-year basic science curricula as perceived by the in-service and pre-serviceteachers who are recipients of the NCE-integrated science curriculum and who also implement the 9-year basicscience curriculum? 4 research questions and 4 research hypotheses guided this study. A sample of 180 pre-serviceand in-service basic science teachers who are recipients of the new 2012 NCCE Curriculum in Integrated-sciencefrom the South – South Geo-political zones in Nigeria were used. A 4-point Likert scale ’24-item questionnaire’called the Curriculum Implementation Synergy Questionnaire was used to obtain data. A cronbach alpha reliabilityco-efficient of 0.75 was obtained. Results showed that a larger proportion of both pre-service and in-service teachersagreed that there was high level of synergy in both curricula. It is recommended that the few topics and laboratoryexercises not found in both curricula should be added and the present basic science teachers who are not recipient ofthe present curriculum should be trained in line with the present curriculum.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".