How burnout, resilience, and engagement interplay among EFL learners? A mixed-methods investigation in the Chinese senior high school context
Bibliographic record
Abstract
English-as-a-foreign-language (EFL) learning is essential for Chinese senior high school students, but burnout can impair their engagement and performance. Resilience, however, can enhance their engagement and shield them from burnout. This mixed-methods study aimed to investigate the interplay between burnout, resilience, and engagement among 413 Chinese senior high school EFL learners. Using AMOS 24, quantitative analyses with structural equation modeling (SEM) revealed the reciprocity between these three constructs. Specifically, burnout was negatively associated with both resilience and engagement, explaining 28.6% and 33.3% of their shared variance respectively. Besides, resilience and engagement were positively correlated, sharing 70.6% of their variance. Qualitative semi-structured interviews with 15 students, analyzed by MAXQDA 2022, supported this reciprocity by specifying the role of each component of these constructs. This study also discusses the theoretical and practical implications of findings and suggests directions for future research.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".