The influence of teacher-student relationships on English teaching effectiveness: Perceptions of students in a Chinese university.
Bibliographic record
Abstract
This thesis examines the important influence of teacher-student relationship on English as a Foreign Language (EFL) from the perspective of Chinese university students. Through questionnaire survey and follow-up interviews among Chinese university EFL students, this mixed method study found strong ties between teacher-student relationship and EFL teaching effectiveness. Based on the findings of this study, I also outline eight qualities that an effective EFL teacher should possess, which can function not only as a guideline for EFL teachers in Chinese universities to follow, and one way to understand Chinese university students as well. The findings from this study suggest it necessary that EFL teachers in Chinese universities should involve their students into the teaching and learning process and improve their teaching in response to students' needs. This study also suggests that Chinese universities should establish a teacher-development system where EFL teachers improve their teaching methodology and exchange information. Chinese students could develop comprehensive English knowledge to meet the challenges of a changing global society. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .G35. Source: Masters Abstracts International, Volume: 44-03, page: 1116. Thesis (M.Ed.)--University of Windsor (Canada), 2005.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".