Applications of data envelopment analysis in education

Emmanuel Thanassoulis; Kristof De Witte; Jill Johnes; Geraint Johnes; Giannis Karagiannis; Conceição S. Portela

doi:10.1007/978-1-4899-7684-0_12

Applications of data envelopment analysis in education

Emmanuel Thanassoulis^*, Kristof De Witte, Jill Johnes, Geraint Johnes, Giannis Karagiannis, Conceição S. Portela

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic

Abstract

Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.

Original language	English
Title of host publication	International Series in Operations Research and Management Science
Publisher	Springer Science + Business Media
Pages	367-438
Number of pages	72
Volume	238
DOIs	https://doi.org/10.1007/978-1-4899-7684-0_12
Publication status	Published - 1 Jan 2016

Keywords

Benchmarking
DEA
Education
Efficiency
Higher education efficiency
Pupil-level effects
School efficiency

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10.1007/978-1-4899-7684-0_12

Cite this

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title = "Applications of data envelopment analysis in education",

abstract = "Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.",

keywords = "Benchmarking, DEA, Education, Efficiency, Higher education efficiency, Pupil-level effects, School efficiency",

author = "Emmanuel Thanassoulis and \{De Witte\}, Kristof and Jill Johnes and Geraint Johnes and Giannis Karagiannis and Portela, \{Concei{\c c}{\~a}o S.\}",

note = "Funding Information: Part of this research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: THALES. Investing in Knowledge Society through the European Social Fund. The views expressed in this chapter are those of the authors and no representation is made that they are shared by any funding body. Publisher Copyright: {\textcopyright} Springer Science+Business Media New York 2016.",

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doi = "10.1007/978-1-4899-7684-0\_12",

language = "English",

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AU - Thanassoulis, Emmanuel

AU - De Witte, Kristof

AU - Johnes, Jill

AU - Johnes, Geraint

AU - Karagiannis, Giannis

AU - Portela, Conceição S.

N1 - Funding Information: Part of this research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: THALES. Investing in Knowledge Society through the European Social Fund. The views expressed in this chapter are those of the authors and no representation is made that they are shared by any funding body. Publisher Copyright: © Springer Science+Business Media New York 2016.

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N2 - Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.

AB - Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.

KW - Benchmarking

KW - DEA

KW - Education

KW - Efficiency

KW - Higher education efficiency

KW - Pupil-level effects

KW - School efficiency

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SP - 367

EP - 438

BT - International Series in Operations Research and Management Science

PB - Springer Science + Business Media

ER -

Applications of data envelopment analysis in education

Abstract

Keywords

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