The Effects of Thematic Progression in Improving Coherence and Cohesion in EFL Writing
Bibliographic record
Abstract
From general observations, the writing produced by EFL students is rather difficult for native English speakers to follow due to its lack of coherence and cohesion. This problem is believed to be minimized by applying Thematic progression theory. This action research was conducted with the purpose to measure to what extent the use of thematic progression could improve coherence and cohesion in writing. Moreover, it also fulfills two sub-tasks which are identifying common theme-rheme problems and clarifying students’ difficulties when applying thematic progression in writing. To reach the answers, the action research was carried out with six-week execution and 20 participants, using both quantitative data (namely numbers of theme-rheme problems and coherence/cohesion scores) and qualitative data (students’ journals). The findings showed that by learning thematic progression, students’ coherence and cohesion band scores could be upgraded by approximately one band. Besides, inappropriate textual theme and empty theme were found to be the main theme-rheme problems. In addition, the shortage of ideas and inflexibility in using grammatical structure were discovered to be hurdles for students to employ the theory in their 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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".