Individual Influencing Factors of L2 Grit: A Structural Equation Modeling Approach
Bibliographic record
Abstract
An increasing number of research is focusing on L2 grit, which plays a significant role in SLA. But few studies examine the factors that affect L2 grit. This study investigates the internal predictors influencing L2 grit in Chinese college students, specifically focusing on L2 willingness to communicate (WTC), L2 anxiety and L2 joy. A structural equation model (SEM) is constructed to examine these relationships. There are 148 valid final questionnaire survey participants. The findings reveal that both L2 WTC and L2 joy positively and directly predict L2 grit; while L2 anxiety has a direct negative effect on L2 grit; and there is a significant correlation among L2 WTC, L2 anxiety as well as L2 joy. This research contributes to the field by promoting further studies on L2 grit, and adds to pedagogical implications for teachers to take appropriate teaching methods that enhance students’ level of L2 grit so as to promote foreign language learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".