Effective Writing Instruction Practices for Students with Learning Disabilities
Bibliographic record
Abstract
Learning Disabilities (LD) is the most prevalent disability among children in Canada (Learning Disabilities Association of Canada, 2007). However, many students with LD are struggling to meet the increasing academic standards and expectations as they advance in school years—one of the reasons is the challenge they have with writing. Writing is part of almost every subject in schools, but could be particularly frustrating for students who have LD; therefore, it is vital for teachers to provide effective writing instruction and support in order to facilitate their writing. This qualitative research study, based on in-depth interviews with two experienced teachers practicing in the Greater Toronto Area (GTA), investigates the following: writing difficulties faced by students with LD; approaches to teaching writing, such as the process writing approach and explicit/direct instruction, that are responsive to students’ writing difficulties; and, accommodations and support, including technology tools, that aid the writing and writing development of students with LD. The analysis of the data collected coupled with the relevant review of literature reveals the kinds of evidence-based strategies and support teachers implement in their writing instruction for students with LD, as well as challenges involved with the practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".