Developing and Validating a Growth Mindset Scale for Young Children (GM-C)
Bibliographic record
Abstract
Beliefs about the malleability of intellectual ability—mindsets—shape achievement. Recent evidence suggests that even young children hold such mindsets; yet, no reliable and valid instruments exist for measuring individual differences in young children’s mindsets. Here, we developed and made freely available an instrument for this purpose—the Growth Mindset Scale for Children (GM-C), suitable for children as young as 4. Among other psychometric properties, we assessed this instrument’s (a) measurement invariance, (b) internal consistency, (c) temporal stability (or test-retest reliability), (d) predictive validity, and (e) cross-cultural robustness in samples of US children (Study 1; N = 220; ages 4 through 6; 50% girls; 39% White) and South African children (Study 2; predominantly grades 4 and 5; N = 331; 54% girls; 100% non-White). The GM-C scale demonstrated invariance across age, as well as strong internal consistency and test-retest reliability. Further, the scale is valid: Four- to six-year-old children with higher GM-C scores oriented toward learning goals (Study 1). Similarly, second- to fifth-grade children with higher GM-C scores oriented toward learning goals, were more likely to take on challenges, and had better grades in math and English (Study 2). These findings suggest that the GM-C is a promising tool for measuring mindsets in young children. We offer practical recommendations on how the scale can be used in future research and discuss theoretical implications of the results.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".