A national megastudy shows that email nudges to elementary school teachers boost student math achievement, particularly when personalized
Bibliographic record
Abstract
In response to the alarming recent decline in US math achievement, we conducted a national megastudy in which 140,461 elementary school teachers who collectively taught 2,992,027 students were randomly assigned to receive a variety of behaviorally informed email nudges aimed at improving students' progress in math. Specifically, we partnered with the nonprofit educational platform Zearn Math to compare the impact of 15 different interventions with a reminder-only megastudy control condition. All 16 conditions entailed weekly emails delivered to teachers over 4-wk in the fall of 2021. The best-performing intervention, which encouraged teachers to log into Zearn Math for an updated report on how their students were doing that week, produced a 5.06% increase in students' math progress (3.30% after accounting for the winner's curse). In exploratory analyses, teachers who received any behaviorally informed email nudge (vs. a reminder-only megastudy control) saw their students' math progress boosted by an average of 1.89% during the 4-wk intervention period; emails referencing personalized data (i.e., classroom-specific statistics) outperformed emails that did not by 2.26%. While small in size, these intervention effects were consistent across school socioeconomic status and school type (public, private, etc.) and, further, persisted in the 8-wk post-intervention period. Collectively, these findings underscore both how difficult it is to change behavior and the need for large-scale, rigorous, empirical research of the sort undertaken in this megastudy.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".