Learner-Centredness in Teachers’ Beliefs: A Qualitative Multiple-Case Study of Chinese Secondary Teachers of English as a Foreign Language
Bibliographic record
Abstract
China’s National English Curriculum Standard, launched in 2001, clearly reflects a philosophy and characteristics of learner-centeredness. However, limited evidence is available as to how far the learner-centred philosophy has, through interacting with the local contexts, influenced teachers’ beliefs, which will translate into a core philosophy and culture affecting teacher behaviours and practices at the school and classroom levels. Drawing on semi-structured interview data of a larger project, this study reports on three secondary school teachers’ overall educational beliefs regarding English teaching, the alignment of their beliefs with learner-centredness and factors influencing their beliefs. The analysis uncovers a wide range of English-teaching related beliefs that positions the three teachers variously on a learner-centred continuum. It also unveils the possible factors that influence teacher beliefs and mediate teachers’ application of beliefs. Factors influencing teachers’ beliefs range from schooling to significant others. Teachers’ reflectiveness is identified as an important influence of teachers’ beliefs and uptake of the curriculum reform. Teachers’ perceptions and responses to a range of contextual factors mediate the application of their beliefs. This study sheds light on the current status of curriculum reform and the uptake of the learner-centred philosophy by teachers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".