Theme and Thematic Progression in EFL Students’ Writing: A Systemic Functional Linguistics Analysis
Bibliographic record
Abstract
A key component of writing that seamlessly integrates into language events is the use of theme and thematic progression. Observing themes is valuable for identifying the writing challenges faced by English learners, while incorporating thematic progression into writing lessons can help students achieve greater coherence in their texts. The present study aims to investigate the dominant patterns of theme and thematic progression in the writing of EFL college students at Universitas Pembinaan Masyarakat Indonesia (UPMI), Medan. A qualitative approach was employed since the study primarily focuses on descriptive elaborations of students’ writing assignments. Data were collected through purposive sampling, involving twenty-one undergraduate students from the Faculty of Teacher Training and Education (FKIP) at UPMI who were enrolled in academic writing classes. Both classroom observations and semi-structured interviews served as the instruments for data collection. Data analysis consisted of several stages. First, the students’ pre-writing assignments were reviewed and categorized into three levels: low, middle, and high achievers. These texts were then analyzed using the framework of Systemic Functional Linguistics (SFL), particularly the theory of theme systems and thematic progression. Subsequently, the students’ post-writing assignments were examined and compared within the same categorization. The findings reveal a significant relationship between students’ writing performance and the application of the SFL framework. High- and middle-achieving students demonstrated the ability to construct coherent messages within clauses, effectively employing theme systems and thematic progression to produce larger texts characterized by cohesion and coherence. This suggests that explicit attention to thematic progression in writing pedagogy can play a crucial role in improving the academic writing skills of EFL learners, particularly in the Indonesian higher education context.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".