Performance Measurement and Quality Assurance in Higher Education: Application of DEA, AHP, and Bayesian Models

Quality assurance (QA) in higher education has become increasingly vital in response to global competition, digital transformation, and evolving sustainability demands. This study examines the leading QA frameworks—namely the European Standards and Guidelines (ESG), the EFQM Excellence Model, and ISO 9001—while integrating advanced analytical methodologies, including Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), and Bayesian modeling, to propose a comprehensive framework for assessing university performance. Through empirical analysis and comparative case studies of internationally ranked universities, this study demonstrates that combining objective indicators with quantitative methods significantly improves institutional efficiency, transparency, and competitiveness. Additionally, the role of digital education, ESG-driven sustainability strategies, and AI-based student feedback systems emerge as being crucial to the effectiveness of QA practices. The results suggest that hybrid evaluation models—blending traditional QA principles with data-driven analytics—promote continuous improvement, optimize resource management, and enhance educational outcomes. This research ultimately highlights the growing relevance of advanced quantitative frameworks in modernizing QA systems and supporting universities in addressing dynamic global challenges.

Authors

Institutions

Publication Details

Journal
Trends in Higher Education
Published
2025-09-18
DOI
https://doi.org/10.3390/higheredu4030054
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Performance Measurement and Quality Assurance in Higher Education: Application of DEA, AHP, and Bayesian Models

G. Sonia Nagy
Trends in Higher Education
Efficiency Analysis Using DEA
article

Performance Measurement and Quality Assurance in Higher Education: Application of DEA, AHP, and Bayesian Models

G. Sonia Nagy
article en

Abstract

Quality assurance (QA) in higher education has become increasingly vital in response to global competition, digital transformation, and evolving sustainability demands. This study examines the leading QA frameworks—namely the European Standards and Guidelines (ESG), the EFQM Excellence Model, and ISO 9001—while integrating advanced analytical methodologies, including Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), and Bayesian modeling, to propose a comprehensive framework for assessing university performance. Through empirical analysis and comparative case studies of internationally ranked universities, this study demonstrates that combining objective indicators with quantitative methods significantly improves institutional efficiency, transparency, and competitiveness. Additionally, the role of digital education, ESG-driven sustainability strategies, and AI-based student feedback systems emerge as being crucial to the effectiveness of QA practices. The results suggest that hybrid evaluation models—blending traditional QA principles with data-driven analytics—promote continuous improvement, optimize resource management, and enhance educational outcomes. This research ultimately highlights the growing relevance of advanced quantitative frameworks in modernizing QA systems and supporting universities in addressing dynamic global challenges.

Trends in Higher EducationVol. 4(3)
University of Miskolc (HU)
Openalex Percentile: Top 70%
Efficiency Analysis Using DEA
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.