Bibliographic record
Abstract
Despite a plethora of studies on practice of Content and Language Integrated Learning in most European countries, few have examined Content and Language Integrated Learning teacher training in China. This study aimed to examine the effectiveness of a Content and Language Integrated Learning teacher training platform for elementary English education in China. It sought to answer the following main research questions: What are the contents differences in Content and Language Integrated Learning teacher training in China? What are in-service English teachers’ perceptions of Content and Language Integrated Learning after the training? And What are the factors which affect the training effect? The qualitative evidence showed that the training content of the teacher platform in China has been flexibly designed following the Chinese elementary English education context, but it also reflected the lack of consideration of teachers’ individual needs in the design of the platform’s training content and the lack of practical sessions and the long-term follow-up support. The statistical evidence showed that teaching experience significantly determines Content and Language Integrated Learning efficacy, and the educational background has little bearing on Content and Language Integrated Learning perceptions. Furthermore, all teachers from different educational backgrounds had a positive perception of their Content and Language Integrated Learning competence. However, Participants believed that their theoretical knowledge and teaching abilities are not equal, which suggests that instructors in this field urgently need help. Further research examining the cultivation of pre-service Content and Language Integrated Learning teachers would be worthy of investigation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.501 | 0.280 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".