Effect of grades one and two language of instruction on performance in Mathematics among grade three pupils in Rulindo district, Rwanda
Bibliographic record
Abstract
Introduction: Prior research has indicated that poor performance in Mathematics may be attributed to the inability of candidates to express ideas in English. There has been little focus on the effect of language use on the learners’ performance in Rwanda schools, where the Language Policy for Education has changed drastically since the country’s independence in 1962. Purpose: This paper sought to find out how the language used for instruction at Grade One and Grade Two could be used to envisage subsequent performance in Mathematics at Grade Three. Methodology: A causal comparative research design of Ex-post facto nature was adopted. The study involved 188 pupils and 6 Mathematics teachers randomly selected. A Mathematics Achievement Test and teachers’ interview guide were used to collect data. Data was analysed using a One-way ANOVA and thematic analysis. Results: The study revealed that pupils whose Language of Instruction was English from Grade One and Grade Two performed significantly better in Mathematics at Grade Three than their counterparts whose Language of Instruction was Kinyarwanda at Grade One and Two. Conclusion and Recommendations: The Language of Instruction at Grades One and Two was the origin for the gap existing among Grade Three pupils who went through the same learning experiences. The findings of this study could help teachers, The Ministry of Education as well as the schools’ management to focus on suitable Language of Instruction to improve the quality of learning. The results of this study could also serve as a springboard for further study in the same area.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".