Bibliographic record
Abstract
This study, carried out at a major technological university in China and based on a convenience sample of 116 students, is designed to identify which aspects of their classroom environments had the greatest effect on the students. Students completed a 26-item questionnaire which elicited general as well as specific views on the EFL classroom environments. The students were divided into English majors (n=64) and non-English majors (n=52). The results showed that, among the items rated as less satisfactory, eight items were similar for both groups, but that the items with lowest satisfaction for each group were significantly different. For English majors textbook selection, learning atmosphere, and teachers’ chalkboard presentation are least satisfactory; for non-English majors interest in target culture, teachers’ chalkboard presentation, and internal motivation are the least satisfactory. The paper concludes that English-language education should be treated differently for majors and non-majors. For English majors, to improve the learning atmosphere, textbook selection and teaching methods need be considered as priorities. English-language education for non-majors should be redesigned with a focus on functional use of the language rather than in-depth study of the target culture.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".