EFL Students’ Burnout in English Learning: A Case Study of Chinese Middle School Students
Bibliographic record
Abstract
This paper aims to explore the English learningburnout of Chinese middle school students to provide solutions to reduce it. Foreign Language Classroom Burnout Scale (FLCBS) is used to make an investigation into 212 middle school students of different grades in No. 10 Middle School in Xi’an city in China. After both qualitative and quantitative analyses of data collected from the questionnaires, it finds out that: 1) a medium level of English learning burnout exists in the students of No.10 Middle School (M=53.80). 2) In terms of grade, three grades have no statistically significant differences in burnout (p=0.377>0.05). 3) As for gender, there is statistically significant difference (p=0.001<0.05). The male’s total burnout is higher than the female’s, especially in Low Efficiency (p=0.006<0.05). 4) There is statistically significant difference in English learning burnout between different majors (p=0.001<0.05). The learning burnout of science students is higher than that of art students, especially in Depletion and Low Efficiency. Based on the research findings, it puts out such suggestions for teachers to lower down students’ English learning burnout as building up students’ confidence, adopting new teaching methods, and improving the relationship between teacher and students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".