A Study of Mindset- Rethinking the Structure of Mindset and How Growth Mindset Interventions are Delivered
Bibliographic record
Abstract
Having a growth mindset (i.e., the belief that traits are changeable with effort), is advantageous in many diverse domain-specific areas, such as academic performance, problem solving creativity, and ability to handle stress and anxiety. With such a variety of different domain-specific mindsets, it is reasonable to question whether there is a general quality underlying where one falls on an overall mindset spectrum. This general mindset, or mind mindset, would be the belief that many traits, attributes, and personality characteristics are changeable with effort. In study #1, we aim to determine whether a mind mindset exists. We will use online self-reports of nine different domain-specific mindset measures to determine if there are correlations across measures within participants. If a mind mindset exists, interventions targeting increasing one’s overall growth mind mindset could potentially influence all other domain-specific mindsets. Similarly, we will investigate ways to effectively deliver growth mindset interventions. Actively engaging in material, by applying the information to one’s life or teaching others, improves retention of that material over passively listening to that material being taught. In study #2, we aim to determine whether an active vs. passive growth mindset intervention is more effective for improving exam scores. Participants will be randomly assigned to one of three groups: 1) Active intervention, 2) Passive intervention, or 3) Control. If there is a mind mindset mediating where one falls on all mindset spectrums, and active interventions are shown to be more effective, vast improvements in growth mindset intervention efficacy may be made. Department: Psychology Faculty Mentor: Dr. Michele Moscicki
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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 | 0.004 | 0.009 |
| 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 | 0.002 |
| 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".