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Record W1638852551 · doi:10.21432/t28c7x

L’intégration des TIC et des aides technologiques par les orthopédagogues oeuvrant auprès des élèves handicapés ou en difficultés d’apprentissage / The integration of ICT and technological support by special-education teachers working with children with a

2012· article· fr· W1638852551 on OpenAlexaffvenueabout
Jean Loiselle, Jean Chouinard

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2012
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPsychologySociologyLibrary scienceArt

Abstract

fetched live from OpenAlex

L'article porte sur l’utilisation des TIC et des aides technologiques par les orthopédagogues oeuvrant auprès d'élèves présentant des handicaps ou des troubles d'apprentissage. Il décrit l'intérêt de ces outils pour ces élèves, puis présente et analyse les résultats d'une enquête menée auprès d'orthopédagogues québécois. Peu d'entre eux rapportent une utilisation régulière des TIC et des aides technologiques par les élèves en classe et la majorité des répondants se sentent plus compétents à utiliser les TIC à des fins personnelles qu'à des fins pédagogiques. L'article discute des moyens pouvant favoriser une utilisation plus soutenue des aides technologiques. This paper studies the use of ICT and technological support by special-education teachers working with children with a handicap or learning disabilities. It describes the interest of such tools for these students before presenting and analyzing the results of a survey done among Quebec special-education teachers. Few report the regular use of ICT and technological support by students in class. The majority of respondents said they felt more competent using ICT for personal reasons than for educational purposes. The paper discusses the means that could facilitate a greater use of technological assistance.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.011
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.286
Teacher spread0.259 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2012
Admission routes3
Has abstractyes

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