Teaching/Learning Resources and Academic Performance in Mathematics in Secondary Schools in Bondo District of Kenya
Bibliographic record
Abstract
The education system in Kenya is evolving steadily even as it is faced with a number of shortcomings which include inadequate teaching/learning resources in secondary schools due to poor planning and corruption. The study looked at the effect of teaching/learning resources on academic performance in secondary school mathematics in Bondo district of Kenya. The research design for this study was descriptive survey design with a total of 405 senior four students as the population of the study. Two hundred and forty two (242) students were randomly selected from nine schools in the three divisions of Bondo districts out of 24 schools. Intact classes were chosen. The schools were stratified into co-educational day, co-educational boarding, boys boarding and girls boarding. One validated research instrument developed for the study was Student Questionnaire on Performance (SPQ) (r = 0.437). Three research questions were answered. The data collected was analyzed using multiple regression analysis. There was a positive correlation among the eight independent variables and the dependent measure – mathematics performance(R= 0.486; F(8,241)=9.014; p
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".