The Effect of Board Games on Speaking Skills of Thai EFL Primary Students
Bibliographic record
Abstract
Speaking skills are considered one of the most crucial skills in learning English as they lay the foundation for effective communication and language acquisition. Proficiency in speaking enables young learners to express themselves, convey ideas, and communicate effectively. This quasi-experimental research aims to investigate the effect of board games on Thai EFL primary students' speaking skills and examine their perceptions regarding the use of board games in enhancing speaking skills. The study included 86 sixth-grade students from two intact classes and assigned them to a control group and an experimental group. The speaking pre-test and post-test were administered to collect data before and after a six-week treatment period. A semi-structured interview was conducted with six participants to examine their perceptions regarding board game instruction. The quantitative data collected from the speaking pre-test and post-test were analyzed using descriptive and inferential statistics, including means, standard deviations, paired-samples t-tests, and independent-samples t-tests. The qualitative data from the semi-structured interview were analyzed using thematic analysis. Data analysis revealed that the experimental group receiving board game instruction obtained higher scores on the speaking tests than the control group. The results showed that there was a statistically significant difference between the two groups at the 0.05 level. In addition, the data from the semi-structured interview revealed participants’ positive perceptions regarding board game instruction as it created a positive classroom atmosphere and enhanced both the functional and emotional benefits for the participants.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".