A study of mindset: better understanding the structure of mindset and how growth mindset interventions are delivered
Bibliographic record
Abstract
Mindsets (MS) (i.e., beliefs about the malleability of traits) exist in many diverse domains, such as intelligence, creativity, emotions, and anxiety. With such a diversity of mindset domains, it is reasonable to question whether a general underlying factor influences all mindsets similarly. For example, if one believes intelligence is malleable, does one also believe creativity, musical ability, and athletic ability are malleable? In study 1, we conducted factor analysis on nine self- report mindset measures to determine if a general mindset factor exists. The nine mindsets studied clustered into three underlying factors: 1) Skills (intelligence, creativity, musical and athletic ability); 2) Personality (personality and morality); and 3) Emotions (emotions and anxiety). Stress did not load onto any of the three factors. In addition, we investigated ways to improve the efficacy of growth mindset interventions. Though growth mindset interventions show positive outcomes, the effect sizes are generally small. Actively engaging in material by applying the information to one’s life, or teaching others, improves retention of that material over passively listening to the material being taught. In study 2, we sought to determine whether an active vs. passive growth mindset intervention is more effective for improving exam scores. We found no significant difference in exam score improvement between the control, active, or passive groups. It is possible that the active intervention was not engaging enough to alter one’s mindset beliefs in only one exposure. Targeting general mindset factors rather than individual mindset domains may improve intervention efficacy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".