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Record W2512995566

Effective Writing Instruction Practices for Students with Learning Disabilities

2015· other· en· W2512995566 on OpenAlexaboutno aff

Bibliographic record

VenueTSpace (University of Toronto) · 2015
Typeother
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationLearning disabilityPedagogyPsychologyComputer scienceMedical educationMedicineDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.224
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.355
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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