Adopting an SFL Approach to Teaching L2 Writing through the Teaching Learning Cycle
Bibliographic record
Abstract
This study applied a Systemic Functional Linguistics (SFL) model to explore how 27 first-year university students in two different English proficiency groups improved their lexicogrammatical choices and metafunctions for writing analytical exposition essays during a 15-week course. To explore how “the teaching learning cycle” influences students’ understanding of the target genre essay, a survey was conducted; furthermore, to explore changes in students’ understanding of metafunctions (ideational, experiential, and textual meanings) of the target genre essay, students’ pre- and post-essays were scored by raters using the SFL framework rubric. Then, six students with lower rating scores at the pre-essay stage from both English proficiency groups were selected to explore how they progressed differently in the target linguistic resources. The results demonstrated that applying an SFL framework of writing assessment to English students’ understanding of essay writing can be used to explicitly examine their improvements.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".