New perspectives on university quality assessment: A Mamdani Fuzzy Inference System approach.

Area of Science:

• Education Policy
• Higher Education Management
• Fuzzy Logic Applications

Background:

• The European Higher Education Area (EHEA) aims for harmonization, but quality criteria remain inconsistent across member nations.
• Assessing university quality is crucial for economic growth, yet faculty perspectives are underrepresented in existing research.
• A gap exists in evaluating public higher education institutions from the viewpoint of their employees.

Purpose of the Study:

• To analyze the quality of the Spanish university system from the faculty perspective.
• To address the research gap concerning faculty-driven quality assessments in public higher education.
• To propose data-driven improvements for university faculty careers and overall educational quality.

Main Methods:

• Utilized a Mamdani Fuzzy Inference System (FIS) methodology, a novel approach for this research area.
• Collected and curated data from four Madrid-based public higher education institutions.
• Analyzed fifteen key elements across five categories: internationalization, scientific production, occupational category, academic background, and professional experience.

Main Results:

• Implemented a Mamdani FIS using MATLAB Fuzzy Logic Toolbox to generate unique quality assessments for each institution.
• Evaluated results to identify which institution demonstrated superior educational quality based on faculty input.
• Provided a quantitative measure of public higher education institution quality through faculty evaluation.

Conclusions:

• The study offers a novel methodology for measuring higher education quality from the faculty viewpoint.
• Findings can inform policymakers in designing strategies to enhance university professor careers.
• Improvements in faculty careers are expected to elevate higher education quality and graduate employability.