An investigation of teacher modeling’s effect on pedagogical competence in public secondary school in Kenya
Bibliographic record
Abstract
Teaching strategies of the 21st century worldwide are ever-changing and need constant upgrading through teacher mentorship practices (TMP) that enhance their pedagogical competence. The purpose of the study was to examine the effect of teacher modeling on pedagogical competence in public secondary schools in Kenya. The study adopted mixed methods research design, grounded in pragmatism research paradigm. The target population was 105,234 teachers including Principals from 8,933 public secondary schools in 47 counties in Kenya. Using the Krejcie and Morgan table (1970) a sample size of 384 was determined with a slight oversampling of 56 to 440 respondents. Teachers in national schools were preferred. Simple random sampling was used to get 36 counties from which 2 national schools were purposively sampled bringing the total number to 72 national schools. Principals from these schools were included in the study. Through purposive sampling 184 teacher mentors who were heads of departments were included in the study while 184 novice/teacher mentees were identified through simple random sampling. The data was collected using Focused Group discussion (FGD) and questionnaires. Qualitative data was analyzed using themes while quantitative data was analyzed using descriptive statistics namely frequencies, mean and standard deviations as well as inferential statistics namely Pearson correlation, ANOVA and simple linear regression analysis. The findings revealed that, teacher modeling has statistically significant effect on pedagogical competence in public secondary schools in Kenya (t= 9.328 (B=0.000) p<.0001). The study concludes that teacher modeling emerges as a multifaceted approach to professional development (PD), offering novice/mentee teachers a blueprint for pedagogical excellence across various dimensions. It recommends that for the purpose of teacher mentorship in public secondary, schools administrators should make teacher modeling a priority to foster their pedagogical competencies.
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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 | 0.006 |
| 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 | 0.001 |
| 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".