Using wise interventions to motivate deliberate practice.
Bibliographic record
Abstract
Deliberate practice leads to world-class excellence across domains. In the current investigation, we examined whether psychologically "wise" interventions targeting expectancies and values-stock antecedents of ordinary effortful behaviors-could motivate nonexperts to engage in deliberate practice and improve their achievement. As a preliminary, we developed and validated a novel task measure of deliberate practice and confirmed its association with (a) expectancy-value beliefs, and (b) achievement in the nonexpert setting (Study 1). Next, across 4 longitudinal, randomized-controlled, field experiments, we intervened. Among lower-achievers, wise deliberate practice interventions improved math performance for 5th and 6th graders (Study 2), end-of-semester grades for undergraduates (Study 3), and end-of-quarter grades for 6th graders (Study 4); the same pattern of results emerged in end-of-quarter grades for 7th graders (Study 5). Following the intervention, expectancy-value beliefs and deliberate practice improved for 1 month (Study 4), but not 4 (Study 5). Treatment proved beneficial over and above 2 control conditions: 1 that taught standard study skills (Studies 2 and 3), and 1 that discussed deep interests, generalized motivation, and high achievement (Studies 4 and 5). Collectively, these findings provide preliminary support for the heretofore untested hypothesis that deliberate practice submits to the same laws that govern typical forms of effortful behavior, and that wise interventions that tap into these laws can spur short-term gains in adaptive beliefs, deliberate practice, and objectively measured achievement. (PsycINFO Database Record
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".