Bibliographic record
Abstract
Writing is an important skill for children’s academic success (e.g., Fitts et al., 2016), underlining the need for effective ESL writing instruction in the elementary classroom (Brisk, 2012; Mohr, 2017; O’Hallaron, 2014). However, there is a paucity of research on elementary ESL writing instruction in Canada. Specifically, we have little understanding about the pedagogical practices in this context. To fill this gap, this paper reports on findings of a study that investigated: (a) factors that influence teacher preparedness, and (b) challenges teachers encounter in teaching ESL writing. Eight elementary teachers, each with at least three years of teaching experience, participated in the study. Data were collected from interviews and online surveys. Findings suggest that teacher preparedness was affected by four factors: (a) background knowledge of teaching ESL writing, (b) professional learning opportunities, (c) self-learning and experience as a teacher, and (d) collaboration, mentorship, and support for teachers. The challenges teachers encountered were grouped into five categories: (a) making sense of the writing curriculum, (b) finding relevant resources, (c) lack of time, (d) difficulty providing feedback, and (e) parental involvement at home. Drawing on these findings, the paper discusses implications and recommendations for ESL writing instruction in the elementary classroom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".