Does MATmatics Work? Impact of Early Numeracy Intervention on Students’ Math Skills
Bibliographic record
Abstract
Early knowledge of numbers, relations, and operations are foundations for student success. Weak foundational number skills can lead to later difficulties, thus early identification and targeted interventions for students with weak number skills can help stop them from falling further behind. In the current study, we tested the effectiveness of MATmatics, a tier 2 intervention that targeted early numeracy skills for at-risk students in grades 2 and 3. Students (N = 45) were quasi-randomly assigned to either an intervention group, or a wait-listed control group. Using a series of 2 (group: intervention, control) x 2 (test timing: pre-intervention, post-intervention) mixed ANOVAS, we found that the math skills (measured with the Early Math Assessment@School numeracy screener) of students assigned to the intervention group improved at a faster rate than the math skills of their waitlisted control peers. The intervention was effective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".