Evidence map›Paper›PMID 41694650›Full record

ArticleF1000Research2025

Toward Effective Educational Supervision in Yemen: A Hybrid Fuzzy Delphi and Clustering Analysis of Technical Barriers.

Riyadh Ghaleb A Alshameri, Adel A Nasser, Abdul Hakim Abdullah, Abed Saif Ahmed Alghawli, Amani A K Elsayed

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Article in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Riyadh Ghaleb A AlshameriDepartment of Education, Sultan Zainal Abidin University, Kuala Terengganu, Terengganu, Malaysia.
Adel A NasserDepartment of Information Systems and Computer Science, Sa'adah University, Sa'adah, Sa'adah, Yemen.ORCID https://orcid.org/0000-0001-5456-1303
Abdul Hakim AbdullahDepartment of Education, Sultan Zainal Abidin University, Kuala Terengganu, Terengganu, Malaysia.
Abed Saif Ahmed AlghawliDepartment of Computer Science, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, Saudi Arabia.ORCID https://orcid.org/0000-0002-6586-2141
Amani A K ElsayedDepartment of Computer Science, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh Province, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Comprehensive educational supervision is essential for ensuring quality teaching, fostering professional development, and supporting institutional capacity building. However, its implementation encounters numerous structural, technical, and human resource challenges. This study aimed to identify, validate, rank, and cluster the technical barriers affecting comprehensive educational supervision in Amanat Al Asimah, Yemen. This aligns with national reform goals by offering strategic insights to improve supervisory systems, thereby enhancing teaching quality, institutional performance, and educational resilience in fragile contexts. Methods: This study employed a three-phase mixed-methods approach. Initially, a literature review identified 11 key barriers to effective supervision. These were validated using the Fuzzy Delphi Method (FDM), involving 16 experienced educational supervisors to assess the consensus and suitability of the items. Subsequently, a quantitative survey targeting 370 teachers was conducted to evaluate their perceived severity. Fuzzy set theory was used to aggregate and defuzzify the responses, generating crisp scores for prioritization. Finally, K-means clustering was applied to segment the barriers based on their impacts. Results: FDM analysis confirmed the validity of all 11 identified barriers, with a domain-level threshold of 0.093 and an average expert consensus of 89.77%, indicating strong agreement. The fuzzy set-based evaluation highlighted three top-priority challenges: weak supervisory competencies, limited ability to develop effective supervisory plans, and poor supervisor-teacher relationships. K-means clustering grouped the barriers into three segments: one high-priority barrier, seven moderate-priority concerns, and three low-priority issues. Notably, weak supervisory competencies emerged as the most critical barrier, isolated in a high-priority cluster. Conclusion: These findings provide evidence-based guidance for policy and strategic interventions aimed at enhancing the effectiveness of supervision systems in fragile educational settings. The study concludes with recommendations for strengthening supervisory competencies, improving resource allocation, and fostering trust-based supervisor-teacher relationships, thereby contributing to the quality of education and institutional resilience in Yemen.

Indexed as

educational supervision; Fuzzy Delphi Method; Yemen; fuzzy set theory; K-means ; clustering; supervisory competencies

Identifiers

PMID41694650
PMCPMC12905534

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