Sustaining growth needs contextual supports: The mindset × ecological-system approach to motivation
Bibliographic record
Abstract
The belief that abilities can be cultivated, commonly referred to as a growth mindset, plays an important role in learners’ motivation and persistence in their educational journey, including learning a new language. Recent research suggests that having a growth mindset alone is insufficient for educational success. Rather, the “seed” of growth mindsets flourishes best when the “soil” of the environment offers students abundant opportunities to apply and implement their growth mindsets in their learning process (i.e., the mindset × context theory). However, discussions about this contextual impact, particularly within the broader sociocultural environment (akin to “climate” in the seed-and-soil metaphor) are limited in mindset research. Therefore, this article introduces the mindset × ecological-system framework by synthesizing emerging research from psychology, education, and applied linguistics, aiming to provide a more comprehensive understanding of how social and cultural factors impact the psychological dynamics of mindsets. This framework illustrates how embedded socioecological systems, ranging from interpersonal to cultural contexts, influence the psychological processes through which mindsets shape learning and resilience. This ecological system framework of mindset serves as a guide for future research to examine how to sustain learners’ growth in diverse sociocultural and achievement settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".