A Study on Reticence in College EFL Classrooms: The Role of Diffusion of Responsibility
Bibliographic record
Abstract
The absence of students’ willingness for classroom participation is known to lead to a lose-lose situation where teaching schedules are disrupted, teachers lose enthusiasm, and students hardly learn anything. The problem is plaguing Chinese college EFL teachers as much as it did decades ago. Large quantities of studies have tackled the problem; however, little research has considered how students’ psychology might link to classroom reticence. One psychological factor accountable for inaction when in the presence of a group of people is diffusion of responsibility. Thus, this study explores whether diffusion of responsibility plays a part in college EFL classroom reticence and whether there are any associations between diffusion of responsibility and gender, or English proficiency, or question type, or class size. Data from a questionnaire, interviews, and classroom observations showed that diffusion of responsibility was indeed a cause for class reticence. Further, Spearman correlation analyses found that no correlation existed between diffusion of responsibility and gender, or English proficiency. Paired-samples t-tests showed class size did have an impact on diffusion of responsibility while question type did not make a difference. Several suggestions on containing diffusion of responsibility and building rapport were put forward. The results should assist EFL teachers to work out possible solutions to the problem of class reticence.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".