A Cross-sectional Comparison of First and Second Year Thai EFL Student Writing: Syntactic, Phrasal, and Lexical Features
Bibliographic record
Abstract
This cross-sectional study compares the written language of Thai EFL students in their first two years of university study. First- and second-year students (N = 170) wrote opinion paragraphs by hand in response to six prompts. Using automated textual analysis tools, clausal (subordination), phrasal (coordinated phrases and complex nominals), and lexical (AWL use and lexical diversity) measures were obtained. Matched-pairs were created by pairing different first- and second-year students from the same faculty of study who responded to the same writing prompt. The results indicated that second-year students produced significantly more complex nominals and AWL words than the first-year students with effect sizes ranging from small to medium. Implications are discussed in terms of pedagogical approaches and assessment in EFL settings, and suggestions for future research are provided.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".