Identifying effective writing tasks for use in EFL write-to-learn language contexts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study compared the effectiveness of two writing tasks at encouraging Thai EFL students (N = 67) to deploy their linguistic knowledge. Students were randomly assigned to respond to one of two writing tasks with different levels of topic familiarity, which was operationalised as ±personal experience. The students completed a Likert-scale questionnaire that elicited their perceptions about the writing task. The paragraphs were assessed using an analytic rubric and were coded for linguistic features relevant to the students' EFL class: accuracy (errors/word), subordination (dependent clauses/independent clauses) and use of future verb forms (simple future, present continuous and going to). The +personal experience paragraphs had higher ratings, greater subordination and more target verb forms, but there were no differences in accuracy. Students reported that they were more able to use their linguistic knowledge when writing about the familiar topic, and there was a positive correlation between their perceptions and text features.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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