A CLOSER LOOK AT GRIT AND LANGUAGE MINDSET AS PREDICTORS OF FOREIGN LANGUAGE ACHIEVEMENT
Bibliographic record
Abstract
Abstract Learning a second/foreign language (L2) is a long process and L2 learners certainly will encounter setbacks and discouragements during this process. However, their reactions to these failures might be different based on their perceptions of L2 learning ability and their subsequent effort put into L2 learning. Based on this, the present study aimed at exploring two underresearched constructs within the field of applied linguistics, namely grit (continuous effort and interest for long-term goals) and language mindset (individuals’ perceptions of their language learning ability). We had five main aims: to examine (a) the factor structure of grit, (b) the factor structure of language mindset, (c) whether there are gender differences in grit or language mindset, (d) the relationships between language mindset and grittiness, and (e) the roles of grit and language mindset as predictors of L2 achievement. To address these aims, a total number of 1,178 university students who were taking general English courses took part in our study and completed the questionnaires. Results of confirmatory factor analysis indicated that the two-factor structure for both grit and language mindset fit the data better than the single-factor structure. We also tested several structural equation models and found that a growth language mindset weakly, but positively, predicted one component of grit (perseverance of effort, or POE), but not the other (consistency of interest, or COI). A fixed language mindset did not predict POE, but did negatively predict COI. Finally, only growth language mindset was a weak, positive predictor of L2 achievement. At the end, theoretical and pedagogical implications regarding the role of grit and language mindset in L2 learning are presented.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".