The Effectiveness of Focused Instruction of Formulaic Sequences in Augmenting L2 Learners’ Academic Writing Skills: A Quantitative Research Study
Bibliographic record
Abstract
Research has increasingly focused on the effectiveness of formulaic sequences in augmenting second language (L2) learners' academic writing skills.These formulaic sequences, which may constitute as much as 52.3% of written discourse (Erman & Warren, 2000), play a vital role in improving L2 learners' writing proficiency and enhancing their performance in academic contexts (Jones & Haywood, 2004;Lewis, 1997).However, experimental testing of such a role is largely undeveloped in research.This quantitative research study is an attempt to investigate the effects of explicitly teaching formulaic sequences on twelve L2 learners' academic writing skills.The study results suggest that an explicit instructional approach to formulaic sequences can enhance their subsequent acquisition.Moreover, formulaic sequences increase L2 learners' writing proficiency because they function as frames to which L2 learners resort when approaching a writing task to compose an academic piece of 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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".