Stakeholder Capacity Building in Monitoring and Evaluation and Performance of Literacy and Numeracy Educational Programme in Public Primary Schools in Nairobi County, Kenya
Bibliographic record
Abstract
To create a radical change within the educational system in public primary schools in Kenya, there is need to invest more on stakeholder capacity building specifically on monitoring and evaluation educational programme. The purpose of this article is to establish the extent to which stakeholder capacity building for monitoring and evaluation influence performance of literacy and numeracy educational programme. Despite numerous initiatives by key stakeholders to better performance of pupils little has been achieved. A descriptive survey research design and correlation design was adapted. Data collected from the respondents by use of questionnaires and interview guide from target population of 2052 and a sample size of 335.Data was analyzed using SPSS version 25 and results presented in tables and figures. Pearson moment correlation coefficient (r) were computed. The coefficient determination of R2 is 0.456 this is an indicator that R2 was the coefficient of determination of this model and it depicted that data collection explained 46%. The remaining 54% was explained by other factors. The overall F statistics 233.446 with p-0.00b<0 0.05 implying there is statistically significant relationship between stakeholder capacity building and performance of literacy and numeracy educational programme. The research suggests that stakeholder capacity building is part of the Participatory Monitoring and Evaluation process, so it must be observed at all stages to ensure educational programme are implemented to the latter by bringing on board all the key stakeholders in education and particularly in literacy and numeracy skills aspects
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".