EFL Students’ Sentence Writing Accuracy: Can “Text Analysis” Develop It?
Bibliographic record
Abstract
Sentence writing is inevitably needed in order to be able to write a longer text because the mastery of writing various types of sentences will facilitate writers to produce a good writing style. However, writing accurate sentences constitute problems for many EFL learners. One way to solve the problems is finding out a teaching strategy that can help the students to learn sentence writing more effectively. This study is an attempt to develop a strategy to teach sentence writing, aiming at knowing the effectiveness of text analysis to enhance the students’ competence to write accurate sentences. An experiment was done in a classroom context by comparing the sentence writing accuracy of the students taught with a teaching strategy covering text analysis (experimental group) and that of the students taught with a teaching strategy without text analysis (control group). The study revealed that there is significant difference between the sentence writing accuracy of the students in the experimental group and that of the students in the control group. The students of the experimental group outperformed those of the control group. It means that text analysis is effective to develop EFL students’ sentence writing accuracy. This is because text analysis is one way to learn grammar; it also strengthens the concept the students have learned. Based on this conclusion, it is suggested that a writing teacher ask the students to do text analysis in teaching sentence writing.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".