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Experiences and Challenges of Implementing the Individualized Education Plan (IEP) for Students with Learning and Intellectual Disabilities in Kuwait

2025· article· en· W4414293375 on OpenAlexvenueno aff
Ohoud Alhajeri

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPlan (archaeology)Sample (material)Inclusion (mineral)Special educationProfessional developmentStratified samplingKey (lock)

Abstract

fetched live from OpenAlex

This study explores the development and effectiveness of Individualized Education Programs (IEPs) in supporting students with special educational needs (SEN). Utilizing a descriptive-analytical approach, the research examines key factors influencing the implementation of IEPs, including teacher expertise, parental involvement, and institutional support. The study sample comprises special education teachers and school administrators, selected using a stratified sampling method to ensure diverse representation. Findings indicate that while IEPs play a crucial role in enhancing student learning outcomes, challenges persist in adapting the curriculum, facilitating interdisciplinary collaboration, and assessing continuous progress. The study also reveals that teachers with specialized training in inclusive education demonstrate greater confidence in designing and executing IEPs, whereas limited parental engagement and administrative constraints hinder effective implementation. The results underscore the need for comprehensive professional development programs, stronger family-school partnerships, and policy reforms to optimize IEP practices. These findings offer valuable insights for educators, policymakers, and stakeholders seeking to improve inclusive education strategies and individualized instructional planning.

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)0.004
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 score0.518
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.076
GPT teacher head0.404
Teacher spread0.328 · 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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