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Record W3205334617 · doi:10.5539/ass.v17n11p91

Predicting the Roles of Attitudes and Self-Efficacy in Readiness Towards Implementation of Inclusive Education Among Primary School Teachers

2021· article· en· W3205334617 on OpenAlexvenueno aff
Nurulhana Zainalabidin, Aini Marina Ma’rof

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessMainstreamInclusion (mineral)Self-efficacyPsychologyMedical educationMainstreamingSchool teachersMathematics educationSpecial educationPolitical scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

Teachers' preparedness is a critical component in implementing inclusive education. It is pertinent to understand whether mainstream instructors are ready for inclusion as the number of children with special needs increases steadily over the years. The Zero Reject Policy has accelerated the implementation of inclusive education in Malaysia. While this is an essential step forward, assessing teachers' readiness for change is critical. This study aims to find out the predictive factors (attitudes and self-efficacy) on the preparedness of mainstream primary school teachers towards the implementation of inclusive education. This study is of a correlational research design where questionnaires were distributed to 367 teachers randomly selected from a cluster of nine schools in Hulu Selangor, Malaysia. The results show that teachers have moderate levels of readiness, attitudes and self-efficacy. There are also significantly positive relationships and predictive correlations between attitudes and readiness as well as self-efficacy and readiness. This implies that attitudes and self-efficacy should be considered in gauging teachers' readiness in the implementation of inclusive education. Taken together, findings in this study could inform further inclusive education research in Malaysia and could be taken into consideration in the design and execution of teacher training courses on Inclusive Education.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.371
Teacher spread0.358 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2021
Admission routes1
Has abstractyes

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