Thai EFL Students’ Writing Errors in Different Text Types: The Interference of the First Language
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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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".