Experiences and Challenges of Implementing the Individualized Education Plan (IEP) for Students with Learning and Intellectual Disabilities in Kuwait
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".