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What about excellence in teaching? A benevolent ranking of universities

Author

Listed:
  • Kristof Witte

    (Maastricht University
    Katholieke Universiteit Leuven (KULeuven))

  • Lenka Hudrlikova

    (University of Economics, Prague)

Abstract

Existing university rankings apply fixed and exogenous weights based on a theoretical framework, stakeholder or expert opinions. Fixed weights cannot embrace all requirements of a ‘good ranking’ according to the Berlin Principles. As the strengths of universities differ, the weights on the ranking should differ as well. This paper proposes a fully nonparametric methodology to rank universities. The methodology is in line with the Berlin Principles. It assigns to each university the weights that maximize (minimize) the impact of the criteria where university performs relatively well (poor). The method accounts for background characteristics among universities and evaluates which characteristics have an impact on the ranking. In particular, it accounts for the level of tuition fees, an English speaking environment, size, research or teaching orientation. In general, medium sized universities in English speaking countries benefit from the benevolent ranking. On the contrary, we observe that rankings with fixed weighting schemes reward large and research oriented universities. Especially Swiss and German universities significantly improve their position in a more benevolent ranking.

Suggested Citation

  • Kristof Witte & Lenka Hudrlikova, 2013. "What about excellence in teaching? A benevolent ranking of universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 337-364, July.
  • Handle: RePEc:spr:scient:v:96:y:2013:i:1:d:10.1007_s11192-013-0971-2
    DOI: 10.1007/s11192-013-0971-2
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    1. Barra, Cristian & Lagravinese, Raffaele & Zotti, Roberto, 2015. "Explaining (in)efficiency in higher education: a comparison of parametric and non-parametric analyses to rank universities," MPRA Paper 67119, University Library of Munich, Germany.
    2. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    3. Mishra, Neelesh Kumar & Chakraborty, Abhishek & Singh, Sanjeet & Ranjan, Prabhat, 2023. "Efficiency analysis of engineering colleges in India: Decomposition into parallel sub-processes systems," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    4. Ruiz, José L. & Sirvent, Inmaculada, 2016. "Common benchmarking and ranking of units with DEA," Omega, Elsevier, vol. 65(C), pages 1-9.
    5. Tommaso Agasisti & Cristian Barra & Roberto Zotti, 2019. "Research, knowledge transfer, and innovation: The effect of Italian universities’ efficiency on local economic development 2006−2012," Journal of Regional Science, Wiley Blackwell, vol. 59(5), pages 819-849, November.
    6. Tommaso Agasisti & Ekaterina Shibanova, 2020. "Autonomy, Performance And Efficiency: An Empirical Analysis Of Russian Universities 2014-2018," HSE Working papers WP BRP 224/EC/2020, National Research University Higher School of Economics.
    7. Juan Antonio Dip, 2021. "What does U-multirank tell us about knowledge transfer and research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3011-3039, April.
    8. Csóka, Imola & Sebestyén, Géza & Neszveda, Gábor, 2019. "Tudományos teljesítmény mérése a magyar felsőoktatás gazdasági képzéseiben [Measuring scientific performance of business and economics faculties in Hungarian higher education]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 751-770.
    9. Cristian Barra & Raffaele Lagravinese & Roberto Zotti, 2022. "Exploring hospital efficiency within and between Italian regions: new empirical evidence," Journal of Productivity Analysis, Springer, vol. 57(3), pages 269-284, June.
    10. José L. Ruiz & Inmaculada Sirvent, 2017. "Fuzzy cross-efficiency evaluation: a possibility approach," Fuzzy Optimization and Decision Making, Springer, vol. 16(1), pages 111-126, March.
    11. Camanho, Ana S. & Stumbriene, Dovile & Barbosa, Flávia & Jakaitiene, Audrone, 2023. "The assessment of performance trends and convergence in education and training systems of European countries," European Journal of Operational Research, Elsevier, vol. 305(1), pages 356-372.
    12. Giannis Karagiannis & Georgia Paschalidou, 2017. "Assessing research effectiveness: a comparison of alternative nonparametric models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 456-468, April.
    13. Barra, Cristian & Lagravinese, Raffaele & Zotti, Roberto, 2018. "Does econometric methodology matter to rank universities? An analysis of Italian higher education system," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 104-120.
    14. Eline Sneyers & Kristof De Witte, 2017. "The interaction between dropout, graduation rates and quality ratings in universities," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 416-430, April.
    15. Csató, László & Tóth, Csaba, 2020. "University rankings from the revealed preferences of the applicants," European Journal of Operational Research, Elsevier, vol. 286(1), pages 309-320.

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    More about this item

    Keywords

    University ranking; Endogenous weight selection; Conditional efficiency; Higher education;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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