Bibliographic record
Abstract
Many accountability systems use data from large-scale assessments to make judgements about school performance. In Ontario, school performance is often assessed using the percentage of proficient students (PPS). The purpose of this study was to shed light on the degree and frequency of changes from year to year in the percentage of proficient students, at a school, in the areas of reading, writing and mathematics for both grades 3 and 6 in Ontario from 2006 to 2010. A second purpose was to assess the influence of cohort size on the variability in scores from year to year. Once schools not having data for 5 consecutive years and outliers were omitted secondary data analysis was used to examine nearly 3000 schools in each subject and grade. For the first part of the study, descriptive statistics and frequencies were the main method of examination. In the second part of the study, variance scores and correlations were used in order to understand the relationship between changes in PPS and cohort size. Findings revealed that changes in school scores from year to year are very large for many schools. Approximately 50 percent of schools experienced changes in PPS greater than 10 percent in any given year. When examining how often, from 2006 to 2010, a school experienced a similar amount of change – generally, both the smallest and largest change categories had a larger percentage of schools experiencing a similar amount of change for two and three years. Very seldom did schools experience the same degree of change in PPS across all 5 years. Results from correlations revealed a significant and inverse relationship between average cohort size and variability in PPS. Considering over 80 percent of schools have 60 or fewer students in a cohort the unpredictability in PPS may prove to be quite frustrating to schools and confusing to stakeholders. Annual PPS scores appear to be a poor indicator of real school performance, and their use to rank or rate schools should be avoided. Recommendations are made about using PPS to report school level results for EQAO, schools and the public.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".