Stakeholder Engagement in Monitoring and Evaluation and Performance of Literacy and Numeracy Educational Programme in Public Primary Schools in Nairobi County, Kenya
Bibliographic record
Abstract
Kenya’s education has faced by many challenges especially in literacy and numeracy skills since the introduction of free primary education. This was contributed by swelling of enrollment in classrooms hence low performance of literacy and numeracy skills. The purpose of this article is to establish the extent to which stakeholder engagement influence performance of literacy and numeracy educational programme in public primary schools in Nairobi County, Kenya. Despite various efforts by key educational stakeholders to improve learner’s performance, minimal achievement have been experienced on learner’s skills. This study adapted descriptive research survey design and correlational research design. Data collected from the respondents by use of questionnaires and interview schedules from a target population of 2053 and a sample size of 335. Data was analyzed using SPSS version 25 and results presented in tables and figures. Arithmetic mean and standard deviation generated from the descriptive data and Pearson moment correlation coefficient (r) were computed. The coefficient of determination R2 is 0.480 this is an indicator that R2 was the coefficient of determination of this model and it depicted that stakeholder engagement explained 48%. The remaining 52% was explained by other factors. The overall F statistics 257.949 with p- 0.000b<0.05 implying a statistical significant relationship between stakeholder engagement and performance of literacy and numeracy educational programme. Interpretations were done and recommendations were policy makers should embrace the methodology of engaging all the stakeholders in programme. This was an indication of strong positive relationship between Stakeholder engagement and performance of literacy and numeracy educational programme. The results showed that stakeholder engagement for monitoring and evaluation strongly influenced the performance of literacy and numeracy educational programme as shown by a correlation coefficient, which was statically significant. Learners should explore more things on their own in order to make predictive answers. Recommendations for further research on participatory monitoring and evaluation practices, which was lacking and specifically involvement of all the stakeholders in the intervention programme in basic education.
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 | 0.007 | 0.010 |
| 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 | 0.001 | 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".