A Study of EFL Curriculum of China’s Science and Technology Institutes under Graded Teaching Model
Bibliographic record
Abstract
Recent years, most universities and colleges have been reforming the English as a foreign language (EFL) curriculum system in China. Some reformed EFL curriculum into English for Specific Purposes (ESP) courses, for instance, while some conducted a graded teaching model in EFL teaching. However, the effect of this reform was not so good, especially at science and technology institutes. Therefore, in view of different opinions to classification of foreign language teaching, the classification scheme of English teaching was improved and rebuilt at first, based on the analysis of the learners’ feedback from the perspective of learner’s needs on the current EFL curriculum system of China’s science and technology institutes under graded teaching model. And then a new EFL curriculum system of China’s science and technology institutes under graded teaching model was designed based on learning-centered approach to course and curriculum design to promote the development of EFL teaching and China’s ESP courses and accordingly meet nation’s needs for cultivating international and integrated high-quality talents of foreign languages.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".