Reticence in Chinese EFL Students at Varied Proficiency Levels
Bibliographic record
Abstract
Reticence in foreign language classes has long been a challenge for both teachers and students. With the advent of globalization, there is a pressing need for EFL (English as a Foreign Language) teachers to help reticent students develop the skills and confidence needed to take an active role in oral English lessons. This article reports on a study of reticence in EFL classrooms in a key university in Beijing. Five hundred, forty-seven first-year non-English majors with three proficiency levels answered a 124-item questionnaire with 20 items on reticence. In addition, one class from each proficiency group was selected for a more focused investigation of reticence and participation in oral EFL lessons. As well as the survey, data gathered during the 14-week term included reflective journals, videotaped observations, and interviews. Analyses of the data revealed that (a) the students were willing to communicate with their peers in English in class and considered speech communication valuable; the more proficient were most positive about interpersonal communication and the most willing to engage in interaction; (b) all the students, irrespective of proficiency level, were the most active during pair work and the least active when responding to teachers’ questions; the more proficient students tended to be the most active in all classroom activities; and (c) with increased exposure to spoken English and more familiarity with the English-learning environment, students at all proficiency levels became (more) active in class. Based on the findings, pedagogical implications are discussed with the aim of enhancing the teaching and learning of spoken English in foreign-language contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".