Thai EFL Students’ Writing Errors in Different Text Types: The Interference of the First Language
Why this work is in the frame
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Bibliographic record
Abstract
This study aimed at analyzing writing errors caused by the interference of the Thai language, regarded as the first language (L1), in three writing genres, namely narration, description, and comparison/contrast. 120 English paragraphs written by 40 second year English major students were analyzed by using Error Analysis (EA).The results revealed that the first language interference errors fell into 16 categories: verb tense, word choice, sentence structure, article, preposition, modal/auxiliary, singular/plural form, fragment, verb form, pronoun, run-on sentence, infinitive/gerund, transition, subject-verb agreement, parallel structure, and comparison structure, respectively, and the number of frequent errors made in each type of written tasks was apparently different. In narration, the five most frequent errors found were verb tense, word choice, sentence structure, preposition, and modal/auxiliary, respectively, while the five most frequent errors in description and comparison/contrast were article, sentence structure, word choice, singular/plural form, and subject-verb agreement, respectively. Interestingly, in the narrative and descriptive paragraphs, comparison structure was found to be the least frequent error, whereas it became the 10th frequent error in comparison/contrast writing. It was apparent that a genre did affect writing errors as different text types required different structural features. It could be concluded that to enhance students’ grammatical and lexical accuracy, a second language (L2) writing teacher should take into consideration L1 interference categories in different genres.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it