Using a genre‐based approach to teach writing to elementary <scp>ESL</scp> students: A boon or a barrier?
Bibliographic record
Abstract
Abstract A genre‐based approach to second language (L2) writing instruction has received traction due to its impact on helping L2 students improve their writing skills. This approach to teaching writing is particularly useful for young English language learners (ELLs), as it simultaneously focuses on their English language and writing skills development. Despite this, the genre‐based approach is often criticized for its apparent prescriptiveness. In this article, we make the case that a genre approach to writing instruction is a boon not a barrier to ELLs in elementary contexts. Drawing on the scholarship in early childhood literacy and L2 writing, we posit that a genre approach to writing instruction is helpful to elementary ELLs for the following reasons: (a) it is a phased approach, (b) it provides experiential learning opportunities, (c) it is goal‐oriented, and (d) it helps make students self‐efficacious and autonomous. Using these reasons as reference points, we also discuss implications for teaching and learning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".