Teachers’ Attitude in Implementation of the Competence-Based Curriculum in Primary Schools in Kericho County
Bibliographic record
Abstract
Competence based curriculum have faced criticism from educationalist based on its tasking and resistivity to change. However, in Canada, Scotland and Finland its implementation has be stunning. The stakeholders in Kenya has raise concern about their preparedness coupled with criticism from section of the government complaining on lack of involvement of stakeholders. The objective of the study was to establish whether teachers’ attitude influences the implementation of the competence-based curriculum. Social constructivism theory was adopted. A descriptive survey design and correlation research design were adopted for the study. The target population of the study included 24 County support Officers (CSOs’), 52 headteachers, and 610 Grade 1 teachers. The sample size was 6 CSOs, 52 Headteachers, and 61 Grade 1 teachers. A saturated sampling technique was used to select all the 52 headteachers from 52 schools. Simple random sampling was used to select the schools and CSOs. A purposive sampling technique was used to select Grade 1 teachers in Kericho County. Data was collected using interview schedules, questionnaires, and an observation schedule. Quantitative data were analysed using descriptive statistics in the form of percentages, means, and standard deviation, while inferential statistics were correlated using Pearson product-moment correlation. Qualitative data were analysed thematically. The attitude of teachers had a positive impact on CBC implementation, with a correlation of 0.560 and a calculated value of 0.00 for the headteachers and 0.284 with a calculated value of 0.032 for Grade 1 teachers. The results of this study are important for the successful adoption of the competency-based program through the participation of education stakeholders.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".