The Use of Simulation Techniques to Enhance English Speaking Skills of EFL Secondary School Students
Bibliographic record
Abstract
This study aims to compare the English speaking skills of EFL secondary school students in Thailand before and after using simulation techniques, to investigate the progress of the students’ speaking skills, and to study the students’ attitudes towards simulation techniques. The study used a quasi-experimental research design with a one-group pre-test and post-test and involved ten grade-eight students in the 2/2021 semester. The participants learned through five simulations: Asking and offering help, Buying and selling, Asking and giving directions, Making a phone call, and Giving suggestions. The data were collected through a speaking pre- and post-test, with a rubric based on pronunciation, fluency, grammar, and vocabulary, and also through semi-structured interviews. The data were analyzed using percentage, mean, standard deviation, inter-rater reliability analysis, Wilcoxon Signed Rank Test, and content analysis. The findings show that the post-test scores were significantly higher than the pre-test scores at P < 0.05 for all simulations. Moreover, the post-test scores for Simulation Five were significantly higher than the post-test scores for Simulation One, indicating the progress of the students’ speaking skills. Additionally, the students expressed positive attitudes towards simulation techniques, as they were able to learn new vocabulary, practice real-life situations, and increase their self-confidence. However, learning through simulations could make students anxious when they are worried about finding the correct and appropriate words. This result suggests that it is essential to build students’ vocabulary knowledge before having them participate in simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".