Enhancing the Mathematics Achievement of Pasifika Students: Performance and Progress on the Numeracy Development Project
Bibliographic record
Abstract
This paper reports on the analysis of data from approximately 30,000 Pasifika students whose teachers participated in the Numeracy Development Project (NDP) between 2002 and 2005. Most students' performance improved from the beginning of the year to the end, and performance and progress seemed to improve from 2002 to 2005. As a result, the gap between European and Pasifika students appeared to reduce fairly steadily over time. These improvements coincided with changes in the composition of the cohort over time, most notably a reduction in the percentage of students from low-decile schools and an increase in the percentage of students from medium- and high-decile schools. Hence, it is difficult to conclude with any confidence that it is the NDP that is primarily responsible for the improvements. Although the gaps in achievement between European and Pasifika students were not completely eliminated, when these differences were put beside those found in other large-scale studies, it was evident that NDP differences were much smaller (a quarter of a standard deviation compared to a whole standard deviation). The use of an individual, orally presented assessment tool with an emphasis on explaining the strategies used to get answers, rather than a written test on which the number of correct answers is simply totalled, may help to explain the positive outcomes for NDP students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".