The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach

Aug 5, 2026

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This study explores key determinants of efficiency in European Higher Education Institutions (HEIs) amid growing challenges and opportunities that demand unprecedented efficiency levels. The research aims to identify drivers of efficiency across European universities by integrating Data Envelopment Analysis (DEA) with machine learning, using feature selection to clarify variable relationships. A multi-country empirical analysis examines efficiency scores and evaluates the impact of student fee funding, Ph.D. program intensity, and international mobility on performance. Findings offer policy implications for improving HEI efficiency.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Abstract

Introduction

Methods: DEA and machine learning feature selection

Empirical analysis and efficiency factors

Policy implications

The underlying signals of efficiency in European universities Citation for published version (APA): Dipierro, A. R. , & De Witte, K. (2025) . The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach. Studies in Higher Education, 50(6), 1306-1325. [https://doi.org/10.1080/03075079.2024.2370948](https://doi.org/10.1080/03075079.2024.2370948) Document status and date: Published: 01/01/2025 DOI: 10.1080/03075079.2024.2370948 Document Version: Publisher's PDF, also known as Version of record Document license: Taverne Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: [www.umlib.nl/taverne-license](www.umlib.nl/taverne-license) Take down policy If you believe that this document breaches copyright please contact us at: [repository@maastrichtuniversity.nl](repository@maastrichtuniversity.nl) providing details and we will investigate your claim. Download date: 09 Jan. 2026 Studies in Higher Education ISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/cshe20)[www.tandfonline.com/journals/cshe20](homepage: www.tandfonline.com/journals/cshe20) The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach Anna Rita Dipierro & Kristof De Witte To cite this article: Anna Rita Dipierro & Kristof De Witte (05 Jul 2024): The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach, Studies in Higher Education, DOI: 10.1080/03075079.2024.2370948 To link to this article: [https://doi.org/10.1080/03075079.2024.2370948](https://doi.org/10.1080/03075079.2024.2370948) Published online: 05 Jul 2024. | Submit your article to this journal | | --- | | Article views: 113 | | View related articles | | View Crossmark data | Full Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=cshe20](https://www.tandfonline.com/action/journalInformation?journalCode=cshe20) STUDIES IN HIGHER EDUCATION [https://doi.org/10.1080/03075079.2024.2370948](https://doi.org/10.1080/03075079.2024.2370948) The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach Anna Rita Dipierro a and Kristof De Witte b,c aDepartment of Economics Sciences, University of Salento, Lecce, Italy; bLeuven Economics of Education Research (LEER), KU Leuven, Leuven, Belgium; cUNU-MERIT, Maastricht, The Netherlands ABSTRAC

FAQs

What is the main objective of the study on European university efficiency?

The study aims to uncover the fundamental elements that drive efficiency in European HEIs by identifying key determinants behind efficiency scores.

How does the research combine Data Envelopment Analysis with machine learning?

It integrates DEA with a machine learning feature-selection technique to shed light on relationships among variables and areas less explored in traditional DEA.

Which factors are examined for their influence on efficiency?

The study highlights the influence of student fee funding, Ph.D. program intensity, and international mobility on achieving high efficiency levels.

Aug 5, 2026

Upload by Levi