Overcoming Low Teaching Self-efficacy of English as a Foreign Language Teachers in Secondary Vocational Schools
Bibliographic record
Abstract
The new English curriculum stand (MOE, 2020) has put forward high requirements for China secondary vocational school English as a Foreign Language (EFL) teachers’ qualities. As part of teachers’ qualities, teaching self-efficacy determines their teaching practices and students’ general achievements. Research has found China secondary vocational school EFL teachers lack confidence in teaching processes (Liu, 2018). Therefore, this study reports on a qualitative study exploring factors contributing to the enhancement of EFL teachers’ teaching self-efficacy in China secondary vocational education context. It is based on the views of 12 experts from Higher Education Institutions, Teacher Training Colleges and the Academy of Educational Sciences, and Secondary Vocational Schools in China to learn more about what factors can enhance EFL teachers’ teaching self-efficacy in China secondary vocational schools. The interview data were examined using thematic analysis which allows for a detailed exploration of the data, and 12 themes emerged from that analysis in terms of experiences, knowledge, and school culture. These themes encompassed various aspects, including enactive mastery experiences, vicarious learning experiences, interactive experiences, physical and emotional states, learners and learning, the pedagogy and curriculum, the English language, the self, vocational background, school spiritual culture, school material culture, and school system culture. The article concludes by considering the implications of the results for those designing policies and professional learning activities for secondary vocational EFL teachers and suggesting potential avenues 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".