GLOBAL RESEARCH TRENDS IN SPECIAL EDUCATION: A BIBLIOMETRIC ANALYSIS
Bibliographic record
Abstract
This bibliometric analysis investigates global research trends in the field of special education over an 11-year period from 2015 to 2025. Special education has increasingly gained prominence due to its role in promoting inclusivity and addressing diverse learning needs, particularly through the integration of digital tools and evidence-based practices. Despite policy advances and technological innovations, disparities in implementation and access persist globally. To examine the evolution of scholarly focus in this area, 939 articles were systematically retrieved from the Scopus database using precise inclusion criteria, including language, publication type, and thematic relevance to students with special needs. The data were cleaned using OpenRefine and further analysed through VOSviewer to identify patterns in keyword co-occurrence, author co-citation, and country-level collaboration. The results revealed a consistent upward trend in publication output, peaking in 2024, with the United States leading in contributions, followed by Turkey, the United Kingdom, and Saudi Arabia. The most frequently cited works address critical themes such as disproportionality in special education placement, teacher attrition, and inclusive pedagogy. Prominent research clusters emerged around inclusive education policies, professional development, social equity, and mental health of educators. Keyword mapping indicated a shift from foundational topics to emerging concerns like e-inclusion, multilingual learners, and burnout among special education teachers. Co-citation and co-authorship analyses highlighted influential authors and collaborative networks shaping the discourse. While the findings underscore growing global interest in special education, they also point to underrepresentation from certain regions, signalling opportunities for broader engagement. This study not only provides a comprehensive overview of the intellectual landscape in special education but also informs future research directions, policy formulation, and professional development strategies to enhance educational equity worldwide.
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.260 | 0.367 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".