The roles of transfer of learning and forgetting in the persistence and fadeout of early childhood mathematics interventions.
Bibliographic record
Abstract
Although many interventions have generated immediate positive effects on mathematics achievement, these effects often diminish over time, leading to the important question of what causes fadeout and persistence of intervention effects. This study investigates how children's forgetting contributes to fadeout and how transfer contributes to the persistence of effects of early childhood mathematics interventions. We also test whether having a sustaining classroom environment following an intervention helps mitigate forgetting and promotes new learning. Students who received the intervention we studied forgot more in the following year than students who did not, but forgetting accounted for only about one-quarter of the fadeout effect. An offsetting but small and statistically non-significant transfer effect accounted for some of the persistence of the intervention effect - approximately one-tenth of the end-of-program treatment effect and a quarter of the treatment effect one year later. These findings suggest that most of the fadeout was attributable to control-group students catching up to the treatment-group students in the year following the intervention. Finding ways to facilitate more transfer of learning in subsequent schooling could improve the persistence of early intervention effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".