An Analysis of Errors in Business-Oriented Written Paragraphs of the Thai EFL Undergraduates
Bibliographic record
Abstract
Effective written communication is not only a crucial skill for academic achievement but also for business context as it could lead to individual professional career success and profitable accomplishment. To achieve these goals requires concise and correct communication. This study, therefore, aims to explore the most frequently-made errors by 30 Business English major students. From the total number of 14,118 words, the study found that the students most frequently made three types of errors: morphological (17.91%), syntactical (45.37%), and mechanical (36.72%) levels. Of all the errors that occurred, article errors appeared to be the most problematic use (17.31%), followed by punctuation (13.34%), plurality (13.43%), capitalization (9.55%), and preposition (8.96%) errors. The findings suggested that mostly-made errors by the Business English major students were influenced by the interference of their first language. Additionally, apart from explicit grammar teaching, greater exposure to the target language is also required in the classroom.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".