The Use of Cooperative Round Robin Discussion Model to Improve Students’ Holistic Ability in TEFL Class
Bibliographic record
Abstract
This classroom action research is carried out within two cycles to breed a strategy on how a Round Robin Discussion Learning Model enhance students’ critical thinking, presentation skills, confidence, and independent learning in Teaching English as a Foreign Language (TEFL) class. Pop-up quiz, teacher made-tests, classroom participation sheet, observation sheet, and rubric are utilized in the study. In addition, the collected data are then scrutinized via quantitative and qualitative doctrine. Quantitative analysis is pertained to gauge students’ critical comprehension on TEFL issues through test, quiz, and worksheet. Meanwhile, for the qualitative analysis, constant comparative method is executed to enlighten parallel variable to depict students’ presentation skills, confidence, and independent learning. The result of the study uncovers that guided question and answer session within and without the group foster students’ higher order thinking skills and the results of the tests, pop-up quizzes indicates significant upgrading from 66% into 82%. Next, round presentation in and out group activity also cultivates students’ presentation skills as well as confidence and independent learning from 65% to 85%, 67% into 77%, and from 65% to 93%. Round Robin discussion model operates well if it is supported with fixed and detailed roles of both students and teacher so that equal interactive practice is well established.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".