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Record W4409211143 · doi:10.6007/ijarped/v14-i2/25068

Implementation of Specialized Vocational Subjects for Students with Learning Disabilities in Career Transition: A Case Study

2025· article· en· W4409211143 on OpenAlexaboutno aff
Mahathir Mohd Sarjan, Marina Ibrahim Mukhtar, Anizam Mohamed Yusof

Bibliographic record

VenueInternational Journal of Academic Research in Progressive Education and Development · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationTransition (genetics)PsychologyMathematics educationMedical educationPedagogyMedicineChemistry

Abstract

fetched live from OpenAlex

Purpose -This study explores the implementation of Special Vocational Subjects (MPV Khas) for students with learning disabilities (MBP) as career transition from teachers' perspectives in the Special Education Integration Program (PPKI).The study employs Maslow's Theory and Behaviorism, using the Transition Programming Taxonomy Model, Transition to Adult Living Model, and the Generic Skills Model from The Conference Board of Canada.A qualitative approach with a multiple case study design was used, involving eight teachers selected through purposive sampling from four secondary schools.Data collection included interviews, observations, and document analysis, which were analyzed descriptively and presented narratively.The findings indicate that MPV Khas implementation in PPKI covers program structure, career experience preparation, generic skills application, parental involvement, and inter-agency collaboration.However, challenges exist in curriculum structure, lack of qualified teachers, financial constraints, student attitudes, and limited parental and agency involvement.A new framework is proposed to enhance the implementation of MPV Khas for career transition in PPKI.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.570
Teacher spread0.429 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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