Graduate Student End-of-Term Satisfaction with Group-Based Learning in EFL Classroom
Bibliographic record
Abstract
The current study explored graduate student end-of-term satisfaction with group learning, compared with traditional instructor-led instruction in EFL (English as a foreign language) classroom. Participants were 74 graduate students, including 33 males and 41 females from a normal university in southern China. The study was carried out with two classes by different teaching methodologies respectively, one was group-based (n/35) with nine groups, and the other was instructor-led class (n/39). Students were assigned randomly to the two types of classes ahead of the formal lessons and taught by the same instructor during the period of an academic term. At the end of the term, a questionnaire survey was administered to all the students of the two classes to measure their satisfaction with English class learning. The results showed students with group-based instruction were more satisfied than those who took the course under the instructor-led format. Also, no significant differences existed between groups with respect to satisfaction. The results of the analysis were discussed and directions for further study were suggested. The significance of the present study lies in the fact that it was able to explore the difference in student satisfaction between group-learning and instructor-led settings in EFL class, and both instructor(s) and students should shift their focus “from what is being taught to what is being learned” in EFL classroom.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".