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Record W325598511

Teachers' attitudes towards inclusion as linked to teachers' sense of efficacy

2008· dissertation· en· W325598511 on OpenAlexaboutno aff
Nadine Randoll

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2008
Typedissertation
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)PsychologyCompetence (human resources)Positive attitudeSpecial needsMathematics educationPedagogySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

In Quebec, meeting the needs of students with special needs and including these children in the general education classroom, is believed to foster their learning and social competence. Teachers have often reported that they do not always feel prepared to teach students with special needs. The purpose of this study was to examine the relationship between teachers' attitudes toward inclusion and teachers' sense of self-efficacy and the quality of the student-teacher relationship. Thirty-four teachers from the region of Montreal, teaching students with special needs in the regular French immersion classroom, responded to four questionnaires and to two open-ended questions, and shared their views and attitudes toward inclusive education. Findings revealed that a positive attitude towards inclusion was related to positive teaching efficacy. Moreover, teachers' attitude varied across disabilities. More specifically, teachers' positive attitude was related to teaching students with academic difficulties and social maladjustments. Teachers' negative attitudes toward inclusion were related to teaching students with behaviour problems and physical disabilities. Teachers also suggested that a variety of resources such as teacher assistants, academic resources and a smaller student-teacher ratio would be beneficial toward successful inclusive practices.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.374
Teacher spread0.334 · 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
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
Published2008
Admission routes1
Has abstractyes

Explore more

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