Brazilian national assessment data and educational policy: an empirical illustration
Bibliographic record
Abstract
In concert with other Latin American countries, Brazil has developed and implemented its own national assessment system for the purpose of monitoring, evaluating and improving their educational system. Prova Brasil is a census-based bi-annual assessment of Portuguese and mathematics achievement of middle school students in Brazil accompanied by four background surveys of students, teachers, principals and schools. This study uses the Prova Brasil assessment data to evaluate the Brazilian educational policy objectives of improved educational achievement through increased school resources using the North-Eastern state of Paraiba, one of the country’s poorest regions, as a sample base. We analysed a sample of 166,354 students in 740 schools over three time points (2007, 2009 and 2011) and two grade levels (grades 4 and 8) separately. Predictor variables were entered hierarchically into two level multilevel models. The study found that infrastructure and academic resources significantly predicted Portuguese and mathematics achievement. This study contributes to the growing body of evidence that school resources have an impact on educational outcomes in the developing world, in contrast to the lack of evidence for impact in developed countries. The study is limited in a number of ways and therefore results should be interpreted with caution primarily due to the cross-sectional nature of the data, the voluntary and low-stakes nature of the testing, and the fluctuating student sample sizes.
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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 | 0.027 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".