The Relationships between Teacher Quality and Sixth Grade Students’ Mathematics Competencies in Kenya and Zimbabwe
Bibliographic record
Abstract
Students should begin to engage in problem-solving and higher order thinking skills in mathematics in the early years of school in preparation for 21st-century technology and problem-solving competencies. Using the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ), this study examines the distribution of significant teacher quality factors related to sixth-grade students’ mathematics competencies across the regions of Kenya and Zimbabwe. The mathematics competencies range from Pre-numeracy to Abstract Problem Solving level. First, we use a multi-level regression model to analyze the relationships between teacher quality and students’ mathematics competencies to find out which teacher quality variables are important for the improvement in students’ mathematics competencies in the participating countries. We then illustrate the distributions of the teacher quality factors within the regions in Kenya and Zimbabwe. From the multilevel model analysis, the teacher quality factors related to students’ increase in mathematics competencies were teaching experience, mathematics competencies, and teachers’ academic qualifications. We observe that students taught by permanently employed teachers had lower math competencies and that the days spent by the teachers in professional development influence students’ mathematics competencies negatively. The distributions of these teacher quality factors that matter in sub-Sahara Africa are concentrated in the capital cities and particular regions in Kenya and Zimbabwe. Implications for policy and practice are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".