The Effects of the Integration of Indirect Feedback and Concordances on Improving Grammatical Accuracy in Thai EFL Students’ Writing
Bibliographic record
Abstract
This study aimed to investigate the effects of the integration of indirect feedback and concordances on improving grammatical accuracy in Thai EFL undergraduate students’ writing and to explore the students’ perceptions toward the intervention. Thirty third-year undergraduate students who studied at a government university in Bangkok were the participants of this study. During the experiment, participants were required to perform three writing tasks. In each writing task, they wrote the first draft and then received both direct and indirect feedback. For indirect feedback, participants were asked to look at concordances to help revise their first draft. The pre-test and the post-test were used to assess grammatical accuracy in the students’ writing before and after the experiment. Additionally, the questionnaire and the semi-structured interview were used to find out the students’ perceptions toward the intervention. The findings revealed that the number of grammatical errors showed a significant decrease in the post-test compared with the pre-test. As a result, the integration of indirect feedback and concordances was seen to be able to improve grammatical accuracy in the participants’ writing. Besides, the results from the questionnaire and the semi-structured interview showed that the participants had positive perceptions toward the intervention on improving grammatical accuracy in their writing because they had a chance to learn from their mistakes after receiving indirect feedback and concordances helped them to induce grammatical patterns.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".