Implementing Competency Based Curriculum (CBC) in Kenya: Challenges and Lessons from South Korea and USA
Bibliographic record
Abstract
This research examines the nature, enactment, and assessment of Competency-Based Curriculum (CBC) models in the United States and South Korea to highlight lessons and strategies that Kenya can utilize to improve CBC implementation. A scoping review of various databases was conducted to search for peer-reviewed articles documenting empirical evidence on implementing and assessing CBC education models in the USA, South Korea, and Kenya. Two researchers from each country screened, extracted the data, and evaluated the records using a custom quality rating scale following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension checklist for scoping reviews. Evidence from the USA and South Korea indicated that the implementation of CBC resulted in improved problem-solving skills, lifelong learning skills, self-efficacy, and autonomy in learners. There is limited evidence from Kenya on the effect of CBC models on learners’ key competencies. Challenges in the three countries include lack of teacher training opportunities, low funding for implementation, inconsistent pedagogical approaches and assessment techniques. The Kenyan government and education stakeholders can address the CBC implementation challenges by using evidence from other studies and countries on teacher training and aligning goals at the school, local authority, regional authority, and national levels.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".