Situated Task-based Language Teaching in Chinese Colleges: Teacher Education
Bibliographic record
Abstract
This study investigated college EFL teachers’ attitudes toward task-based language teaching (TBLT), regarding their familiarity with the idea of TBLT, their actual use of TBLT, and contextual factors that impede the implementation of TBLT in the higher education context in China. The study described here is a questionnaire survey with 26 valid responses. Results of this study are derived from discussion concerned with qualitative and quantitative data. The findings in the study show that though there are constraints from various aspects (including, the teaching materials, large class size etc.) for the successful implementation of TBLT, TBLT as a communicative teaching approach received very positive feedback from teachers. The majority of the teachers in this study hold positive views towards TBLT even though they have a low-level understanding of principles and practices of TBLT. The results addressed the issues existed in the pre-service and in-service training of Chinese EFL teachers. This study also highlighted the need for the Chinese ELT teachers to further develop their professional skills in terms of their competence to deal with large class size teaching, material development and English proficiency. Based on the findings, suggestions for teacher education and further development were made. This research is intended to yield informative insights regarding sustainable curriculum change management, policy implementation and professional development of English teachers in the Chinese EFL context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".