ESL Writing Instruction in K-12 Settings: Pedagogical Approaches and Classroom Techniques
Bibliographic record
Abstract
Writing is an important literacy skill that K-12 students must develop for academic success. For young ESL students, developing writing skills entails both learning English and developing writing as a literacy skill. The need for this dual skill development underlines the challenges of teaching K-12 ESL writing, as teachers must strike a balance between teaching writing as a tool for students’ English language development and literacy skill. This paper reports on findings related to pedagogical approaches and classroom techniques that are prevalent in K-12 ESL writing instruction. Our research is based on a systematic review of 49 studies published between 2010 and 2019. Using content analysis, three pedagogical approaches were identified: (a) approaches centered on teacher perspectives, (b) approaches centered on student perspectives, and (c) approaches centered on emerging research and theories of ESL writing instruction. As well, the analysis yielded four classroom techniques: (a) adopting SFL-oriented and genre-based activities, (b) utilizing ESL-bilingual student writers’ language learning traits, (c) incorporating digital technology, and (d) adapting instructional practices in response to student needs. Critically reflecting on these pedagogical approaches and classroom techniques, the paper discusses the advantages and challenges of implementing them in the classroom. The paper provides a taxonomy of instructional practices that K-12 ESL writing teachers may find useful.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".