Comparison of Measures of Mathematical Achievement: EMA@School and Star Math Assessment
Bibliographic record
Abstract
There is a need for valid and reliable screeners to assess students’ early math skills and help educators identify students who are at-risk for math difficulties. The Early Math Assessment @School (EMA@School) has been used to test the foundational number skills (i.e., numbers, relations, and operations) of over 200,000 Canadian students. In this study, we compared the structure, content, and student performance on the EMA@School to STAR Math, a curriculum-based math screener. Students (N = 230) in Grade 3 completed the EMA@School once (Fall 2023) and Star Math twice (Fall 2023, Winter 2024). EMA@School scores were strongly correlated with StarMath scores. Moreover, skills in the three foundational subdomains of number, relations, and operations, all uniquely predicted performance on StarMath. Finally, most students classified “at-risk” based on the EMA norms were also “at-risk” based on StarMath norms. Together, these findings provide convergent and criterion validity for the EMA.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".