Application of Production-Oriented Approach in College English Instruction in China: A Case Study
Bibliographic record
Abstract
With effective learning as its core principle, Production-oriented Approach (POA) was developed to address the problems of English classroom instruction in China, such as text-centeredness, the separation of learning and using and “dumb English”. This study applied POA to college English classroom instruction in order to examine its effects on English learning and explore its implications for English instruction in the EFL context. Twenty-two second-year students majoring in Applied English in a Sino-US cooperative education program participated in the study. Data were collected through questionnaires distributed to the students at the end of each unit and the semester, and semi-structured interviews with fifteen participants to elicit information about students’ motivation, engagement, reflections upon their learning process, and perceptions on the POA class. A variety of assessment tools, including the Teacher-Student Collaborative Assessment approach, were applied to evaluate students’ performance and progress. The study revealed that POA played a positive role in stimulating students’ learning motivation and enhancing students’ communicative competence, especially in speaking and writing. However, the implementation of POA should also be adapted to learner’s variables and needs so that POA can realize its values and create successful results in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".