Improving the English Speaking Ability of Sixth Grade Thai Students Using the Role-play Technique
Bibliographic record
Abstract
Speaking is one of the fundamental abilities that students should develop. In fact, the students are struggling in speaking English due to several factors such as fear of making grammatical errors, lack of confidence or limited vocabulary knowledge which led to low motivation to practice their speaking skills. The aims of this study were to investigate how the role-play technique improves the students’ speaking abilities and to explore students’ opinions towards the use of role-play techniques. This study employed pre-experimental research with a target group using a pre-test, post-test, and a questionnaire. The participants in the study were selected by employing a purposive random sampling method, which consisted of 24 sixth grade students from Ban Namon school, Kalasin province during the first semester of the academic year 2022. The findings of the study were analyzed using SPSS to calculate the t-test score, mean and standard deviation. The research results revealed a great improvement from the pre-test to post-test from 9.38 to 14.79. It showed that role-plays can help students gain more confidence as well as being able to speak more fluently in public. It was found that they were motivated by the fun atmosphere in the classroom. Moreover, role-plays can be an alternative technique for teaching speaking because students gain a direct experience of using the expressions they have learned in different situations.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".