Use, Errors, and Self-Perceptions of Thai EFL Learners with Conditional Sentences
Bibliographic record
Abstract
Conditional sentences are difficult for EFL students to understand because of their complexity in both form and function. By analyzing the performances and opinions among EFL learners, the pedagogical contribution may be beneficial for both EFL students and teachers. The use, errors, and perceptions of Thai EFL students regarding conditional sentences were explored in this study. Instruments of the study included a chapter test, writing assignments, and an online survey. Data were analyzied by means of Google Form and AntConc software. The results of the test revealed that the participants performed best on the zero conditional type, while the first conditionals were used the most in their writing. Findings of the error analysis revealed that some difficulties in the use of tense were widely occurring such as using present progressive instead of present simple tense on the if-clause for zero type. Following that, the participants believed that the second conditionals were the most difficult, while the zero conditionals were the easiest. 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".