The Effect of Group Dynamics-Oriented Instruction on Developing Iranian EFL Learners’ Speaking Ability and Willingness to Communicate
Bibliographic record
Abstract
The study investigated how group-dynamics instruction techniques of adaptable nature can be to the benefit of EFL (English as a Foreign Language) learners so as to develop and improve their willingness- to- communicate and speaking- ability in the long run. After analyzing the data via ANCOVA and EFA, the researcher selected 108 young Iranian male and female EFL learners in a language school in Tehran by means of convenient sampling technique. This investigation shows how EFL learners reacted to Group-Dynamics Oriented Instruction (GDOI). Later, the researchers instructed speaking tasks along with improving the learner’s willingness to communicate. TOEFL PBT Test was run among participants for homogeneity purposes, and then the researchers used two parallel speaking section of PET test along with WTC questionnaire before and after the treatment process. The findings of the study bore witness to hypotheses of the study, indicating that GDOI was reliably effective in improving speaking ability and uplifting willingness to communicate. In the same line of analysis, the researcher proved that GDOI has improved EFL learner’s willingness to communicate since GDOI provoked and triggered energy, interest, and inclination to partake in discussions in learners. As its effects on speaking ability were concerned, the results were interpreted as showing that GDOI would exert changes to L2 learners’ conceptual and psychological predispositions that, in return, would determine the strategies and behaviors the learners employ to address the challenges of L2 learning.
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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 | 0.005 |
| 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 | 0.000 | 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".