Current English Language Teaching Using Production-Oriented Approach: A Systematic Review
Bibliographic record
Abstract
In recent years, the production-oriented approach (POA) has grown to be a popular research topic in China. Despite that, it is noticeable that a systematic literature review of the international journals written in English (2019-2023) on POA is not available, which has raised a need to promote POA to other parts of the world, especially to benefit more English learners. This review is focused on three questions: What is the present state of implementation of POA? What are its influences on students’ acquisition of the English language? What are the obstacles encountered in promoting broader adoption of POA? Based on a search via three databases (Scopus, WoS, and ERIC), 35 journal articles between 2019 and 2023 were analysed finally. The findings indicate that: (1) POA research is now mainly empirical research, related to theory development, teacher development, and textbook development. (2) The effects of POA on English learners are as long as they are manifested in terms of their effects on students' writing ability, speaking ability, translation ability, positive affective experiences and learning awareness. (3) At present, the main problems encountered by POA lie in the limited classroom teaching time, the relatively small sample size, and the remaining gap between the selection of teaching materials and the teaching design to keep students’ continuous motivation. In addition, this article also discusses the future direction of research, which needs to work on linguistics, literature, western culture, ESP programs and other languages, as well as the integration and development of teaching materials and teacher development.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".