K-12 ESL Writing Instruction: Learning to Write or Writing to Learn Language?
Bibliographic record
Abstract
Writing is an important literacy skill for K-12 students’ academic success. For English as a Second Language (ESL) children, developing writing skills involves both learning English and learning to write. This makes ESL writing instruction challenging as teachers have to strike a balance between teaching writing as a literacy skill and as a tool for students’ English language development. Recent research has identified that in-service teachers in K-12 settings lack requisite training in L2 writing, resulting in various challenges in the ESL writing classroom. One such challenge for them is to determine whether the focus of writing instruction should be to teach students how to write (learn-to-write) or to utilize writing as a tool to help students develop the English language (write-to-learn language). Eliciting the theoretical orientations of both learn-to-write (LW) and write-to-learn language (WLL), this article suggests that the LW and WLL approaches are not mutually exclusive for teaching ESL writing. Based on a review of recent research, the paper discusses a systemic functional linguistics (SFL)-informed genre-based writing pedagogy as well as teaching and learning activities that integrate both LW and WLL principles into ESL writing instruction in the elementary classroom.
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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 | 0.001 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".