Promoting Thai EFL Learners’ Ability to Self-correct Errors in Written English Sentences through Games
Bibliographic record
Abstract
Games have been widely accepted as an effective tool for language learning. They help learners achieve better learning outcome and create a learning atmosphere which contribute to learners’ learning. The present study, hence, employed games to help EFL learners write better in English. In the present study, games were used to encourage Thai EFL learners to self-correct errors found in their English sentences. After five weeks of learning with games, the learners’ ability to self-correct errors was observed. Their posttest average score (x =18.65, S.D.=6.05) was higher than the pretest one (x =13.58, S.D.=6.45). The results from the paired samples t-test indicated a statistically significant difference at the 0.01 level which meant that games helped promote this group of learners to self-correct errors in written English sentences. Furthermore, the learners reported that they enjoyed English writing classes with games because they motivated them to learn English in a relaxing class. The learners’ good interaction and collaboration were also observed during the games. The findings from this study imply that games should be incorporated in language classes for learners’ positive learning outcome.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".