TEACHER PERCEPTIONS AS A PREDICTOR OF CBE IMPLEMENTATION IN KENYA: A CASE OF PRE-PRIMARY SCHOOLS IN MIGORI COUNTY
Bibliographic record
Abstract
Abstract: Teacher perceptions significantly influence implementation of curricula. Competency-Based Education (CBE), in Kenyacould not be any different. teacher ‘s positive perceptions that could stem, from adequate training, limited resources, and a clearunderstanding of the curriculum, can be associated with the successful or failure in CBE implementation. Conversely, negativeperceptions, often linked to perceived inadequacies in training or resources, can hinder the process. This study explores the perceptions of pre-primary educators regarding the implementation of the Competency-Based Curriculum (CBC) in Kenya. With the CBC now making a paradigm shift from sheer nurturing of talent to the actual learning outcomes(use of the talents), understanding the views of early childhood educators of the same remains critical, as they serve as the foundation for lifelong learning. The research examines the extent to which pre-primary school teachers understand CBE principles, their preparedness, the resources available, and the opportunities and challenges they encounter during implementation. A qualitative research design was adopted, using interviews and focus group discussions with pre-primary teachers across selected public and private schools. Findings reveal a mixture of optimism and concern among educators -while many appreciate the learner-centered and skills-oriented approach of the CBE, they also report challenges related to limited training, insufficient instructional materials, and high teacher-pupil ratios. The study recommends targeted professional development, improved resource allocation, and supportive policy frameworks to enhance effective CBC implementation at the foundational level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".