Mindsets Matter for Linguistic Minority Students: Growth Mindsets Foster Greater Perceived Proficiency, Especially for Newcomers
Bibliographic record
Abstract
Abstract Growth language mindsets (i.e., beliefs that language ability can be improved) are found to sustain learners’ motivation and resilience in challenging situations. Considering that migrants who are speakers of languages other than the dominant ones often face challenging daily communications, we examined important but understudied questions of ‘how’ and ‘when’ growth language mindsets predict migrants’ language experiences, including language anxiety, language use, and perceived English proficiency. In 3 studies, we surveyed 2,163 foreign‐born university students in Canada who indicated English as their second language. We found that growth language mindsets positively predicted self‐assessed English proficiency, even 4 months after the initial assessment of mindsets. Answering ‘how,’ we found that migrants with stronger growth mindsets were less anxious, were more likely to use English, and reported higher proficiency, even after accounting for baseline proficiency. Concerning ‘when,’ we found that mindsets have significant and moderate association with language use, anxiety, and perceived proficiency for only more recently arrived students (who lived in the receiving country for less than 7 years). Although newly arrived migrants are more anxious about using English and less likely to use English, they are resilient when they envision growth in their new language. Growth mindsets may help English as a second language (ESL) students thrive in intercultural communication and succeed in language development.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".