Why do growth mindsets make you feel better about learning and your selves? The mediating role of adaptability
Bibliographic record
Abstract
We argue that growth (vs. fixed) mindsets are important for positive emotions and self-development because growth mindsets can foster adaptability, referring to the ability to adjust oneself in different circumstances. This study examines the role of mindsets in adaptability and whether adaptability, in turn, predicts learning emotions (anxiety and enjoyment), self-concept, and self-efficacy. The data were collected through self-report questionnaires from 211 (141 females and 70 males, Mage = 17.2 years, SDage = 6.8) Iranian intermediate language learners. The path analysis results showed that fixed mindsets negatively predicted anxiety, enjoyment, self-concept, and self-efficacy through the mediation of adaptability, whereas growth mindsets positively predicted enjoyment, self-concept, and self-efficacy and negatively predicted anxiety through adaptability. The results held even after accounting for ideal L2 self and perceived competence. These findings highlight that growth mindset is an essential factor for developing positive learning emotions and self in foreign language classrooms.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".