Learning Mathematics with Interactive Technology in Kenya Grade-one Classes
Bibliographic record
Abstract
While countries in sub-Saharan Africa have made significant progress towards achieving universal school enrolment, millions of students lack basic numeracy skills. This paper reports the results of a pilot study that aimed at using the Emergent Literacy in Mathematics (ELM) software to teach mathematics in early primary grades in Kenya. Designed as a pre- and post-test non-equivalent group research, the study unfolded in 14 grade-one classes from 7 primary public schools. After having learned with ELM for about two terms, the experimental students (N = 283) considerably outperformed their peers (N = 171) exposed to traditional instruction with the effect sizes of +0.37 on the overall skills measured by a standardised test of mathematics. The impact of ELM activities was the greatest on students’ ability to take language and concepts of mathematics and apply appropriate operations and computation to solve word problems. On this set of skills, the magnitude of difference between the experimental and control groups was +0.77. This study also revealed some positive shifts in the teachers’ perceptions about their practice. The teachers who adopted ELM in their practice reported having gained more confidence in mathematics and comfort in teaching mathematics with computers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".