Empowering Educational Leaders: Nurturing Strong Teaching and Learning Environments in Kenyan Schools.
Bibliographic record
Abstract
Purpose: Kenya’s paradigm shifts in curriculum from content based to competence-based curriculum is geared towards improving the quality of education to meet the global demands. This prompts the need to build a firm foundation from early childhood education through appropriate classroom learning experiences and good leadership. This study explored educational leaders' practices in their management of primary schools in Kenya. Methodology: This article draws evidence from the Longitudinal Study which was designed among other things to establish the best practices that teacher educators can adopt to inform the professional learning of Student Teachers to become effective champion teachers, and leaders. The study adopted a mixed method approach with a sample size of 29, comprising 4 education officers, 25 head teachers in Mombasa and Kilifi counties in Kenya. Findings: The findings revealed that most education leaders have embraced transformative leadership styles and policies to motivate their subordinates through action research, portfolio, mentorship, coaching, collaboration, and networking. Findings revealed further that mentorship is key in educational leadership and thus should be regarded as a vital aspect in teacher education and in professional learning. Data also indicated that leadership styles differ in relation to the context. Unique Contribution to Theory, Policy and Practice: The leader’s role is deeply involved with setting the school’s direction; thus, teacher leadership preparation is essential for successful implementation of education processes and programs. Hence, we recommend that school leaders should be adequately prepared to face the tasks and the accompanying contextual challenges they face once assigned the responsibilities to lead institutions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".