Measuring Language Mindsets and Modeling Their Relations With Goal Orientations and Emotional and Behavioral Responses in Failure Situations
Bibliographic record
Abstract
Some people ascribe successful language learning to an innate aptitude that cannot be further developed, at least after a certain young age (i.e., an entity mindset), while other people believe that language learning ability can be improved (i.e., an incremental mindset). The purpose of this research is to (a) introduce the Language Mindsets Inventory (LMI), and (b) test the mindsets–goals–responses model, which maintains that learners’ mindsets predict the goals that they set for language learning, and that these goals in turn affect how they respond to difficult academic and communication episodes. Correlational and factor analyses provided evidence of the LMI's valid and reliable use in research with university‐level language students. Path analyses showed that regardless of their competence level, greater endorsement of an incremental mindset was associated with the goal of learning more about the language, and this learning goal in turn predicted greater mastery and less helpless responses in failure situations. Greater endorsement of an entity mindset predicted the goal of demonstrating competence (i.e., performance approach goals) when students believed that they had stronger language skills. The use of the LMI in future research and the importance of supporting incremental mindsets for language education are discussed.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".