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Best-Worst PROMETHEE method for evaluating school performance in the OECD's PISA project

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  • Ishizaka, Alessio
  • Resce, Giuliano

Abstract

This paper introduces the Best-Worst PROMETHEE method, which avoids the rank reversal problem of other PROMETHEE methods. We use this new technique to rank schools in the Programme for International Student Assessment (PISA). We consider three separate outputs, provided by average student attainments in mathematics, language, and sciences. Our sample comprises 16,500 schools in 66 countries, and our results show significant differences between school performance both within and between countries. The top half of the ranking mainly comprises European and Asian countries, while the bottom half includes many North African, Middle Eastern, and South American countries. We find no strong association between inequality between schools and the country-level performance, suggesting the absence of a trade-off between equity and performance in education. The Best-Worst PROMETHEE is a generic method that can be used to support decisions in strategic sectors with multidimensional outcomes.

Suggested Citation

  • Ishizaka, Alessio & Resce, Giuliano, 2021. "Best-Worst PROMETHEE method for evaluating school performance in the OECD's PISA project," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:soceps:v:73:y:2021:i:c:s0038012119301818
    DOI: 10.1016/j.seps.2020.100799
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    Cited by:

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    2. František Zapletal, 2022. "Revised PROMETHEE algorithm with reference values," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 521-545, June.
    3. Zhang, Jingshun & Hu, Jiayi & Wang, Xitong & Fang, Lien & Jin, Yi & Li, Muyang & Liu, Yangqing & Wu, Anna & Wang, Libin & Liu, Ruining & Zhang, Yi & Chen, Faan, 2023. "Quantifying transport safety success at the regional level: A guide to policy and practice on investment for G20," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    4. Pranith Kumar Roy & Krishnendu Shaw, 2021. "A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    5. Bao, Tiantian & Liu, Yifan & Yang, Zhongzhen & Wu, Shanhua & Yan, Zhenli, 2024. "Evaluating sustainable service quality in higher education from a multi-stakeholder perspective: An integrated fuzzy group decision-making method," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    6. Gul, Muhammet & Yucesan, Melih, 2022. "Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).

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