Predictive Models for Grade 9 Mathematics Stream: Controlling for Past EQAO Performance
Bibliographic record
Abstract
In 2021, Ontario replaced Academic/Applied streaming with a unified grade 9 mathematics curriculum. This decision was presented as an effort to reduce inequity in the school system. However, the existing literature fails to control for prior performance under a standardized measure. We utilize confidential student-level data from the province’s EQAO grade 3, 6, and 9 assessments to evaluate predictive factors associated with the applied mathematics stream. Data is filtered to consider students from 2006-2019 who: participated in both the grade 3 and grade 6 EQAO assessments, appear in the grade 9 data, and did not skip or repeat grades. We employ a linear probability model with fixed effects and clustered standard errors to predict whether a student takes applied grade 9 math based on EQAO data for grades 3 and 6, including: performance on EQAO assessments; Individual Education Plan (IEP) status; French Immersion (FI) status; whether the student identifies as male; first language; English Second Language (ESL) program status; and whether the student was born outside of Canada. Findings indicate that, holding other variables constant, having an IEP is associated with a statistically significant increase in the probability that a student is enrolled in the applied mathematics course when they first participate in the grade 9 EQAO assessment. We also find positive (and highly statistically significant) estimated coefficients when breaking up IEP status by indicators for specific underlying IPRC exceptionalities, and IEP with no exceptionality. Furthermore, results are robust under logistic regression. Our findings potentially provide empirical support for the province’s decision to end grade 9 mathematics streaming. Even after controlling for past performance, our findings suggest that student characteristics in prior grades – especially IEP and IPRC exceptionality status – considerably influence predicted stream, potentially limiting student’s future career paths and opportunities for higher education.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".