Abstract
In the tertiary education institutions, rankings have started gaining ample attention all over the world. This has created a profound impact on the indian higher education system. As a result of that, in 2015, the government of India announced National institutional ranking framework (NIRF) to rank the indian institutions. NIRF is based on multiple parameters which are evaluated by standardised survey. In this work, a mathematical model, which can handle such multiple parameters to rank higher education institutions (HEIs), has been proposed. In this model, six criteria, named as, student intake, faculty strength, expenditure of the institution, research paper published per faculty, placements and perception, are considered. Since the criterion perception is a qualitative criterion and can not be modelled by classical mathematics, fuzzy rule based inference system is proposed to determine its precise value. Then DEA-Entropy-TOPSIS approach has been employed to rank HEIs. To emphasize the applicability of the proposed method, a numerical illustration is provided. It is asserted that this proposed mathematical model is a unique HEIs ranking approach involving human perception.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbott M, Doucouliagos C (2003) The efficiency of Australian universities: a data envelopment analysis. Econ Educ Rev 22:89–97
Abramo G, Cicero T, D’Angelo CA (2011) A field-standardized application of DEA to national-scale research assessment of universities. J Informetr 5(4):618–628
Athanassopoulos A, Shale E (1997) Assessing the comparative efficiency of higher education institutions in the UK by means of data envelopment analysis. Educ Econ 5:117–134
Avkiran NK (2008) Investigating technical and scale efficiency of Australian Universities through data envelopment analysis. Soc Econ Plan Sci 35:57–80
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444
Flegg AT, Allen DO, Field K, Thurlow TW (2004) Measuring the efficiency and productivity of British universities: an application of DEA and the Malmquist approach. Educ Econ 12:231–249
Hwang CL, Yoon KP (1981) Multiple attribute decision making methods and applications: a state of the art survey. Springer, New York
Johnes J, Johnes G (1995) Research funding and performance in UK university departments of economics: a frontier analysis. Econ Educ Rev 14(3):301–314
Johnes J (1996) Performance assessment in higher education in Britain. Eur J Oper Res 89:18–33
Johnes J (2006) Measuring teaching efficiency in higher education: an application of data envelopment analysis to economics graduates from UK universities 1993. Eur J Oper Res 174:443–456
Kao C, Hung HT (2008) Efficiency analysis of university departments: an empirical study. Omega 36:653–664
Korhonen P, Tainio R, Wallenius J (2001) Value efficiency analysis of academic research. Eur J Oper Res 130:121–132
Kuah CT, Wong KY (2011) Efficiency assessment of universities through data envelopment analysis. Procedia Comput Sci 3:499–506
Lai W-H, Tsai C-T (2009) Fuzzy rule-based analysis of firm’s technology transfer in Taiwan’s machinery industry. Expert Syst Appl 36:12012–12022
Li G (2011) Output efficiency evaluation of university human resource based on DEA. Procedia Eng 15:4707–4711
Ross TJ (2010) Fuzzy logic with engineering applications. Wiley, London
Shih H-S, Shyur H-J, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Modell 45(78):801–813
Tyagi P, Yadav SP, Singh SP (2009) Relative performance of academic departments using DEA with sensitivity analysis. Eval Program Plan 32:168–177
Worthington AC, Lee BI (2008) Efficiency, technology and productivity change in Australian Universities, 1998–2003. Econ Educ Rev 27:285–298
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zarrazvand H, Shojafar M (2012) The use of fuzzy cognitive maps in analyzing and implementation of ITIL processes. Int J Comput Sci Issues 9(3):511–524
Zimmermann H-J (1991) Fuzzy set theory and its applications. Kluwer, Boston, MA
Acknowledgements
The authors would like to thank Prof. Ashok Deshpande chair-BISC-SIG-EMS for his valuable inputs and suggestions. We thank the editor and the anonymous reviewer for their insightful comments, which helped in improving the paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Srinivasan, R., Jain, V. & Dharmaraja, S. Perception based performance analysis of higher education institutions: a soft computing approach. Soft Comput 24, 513–521 (2020). https://doi.org/10.1007/s00500-019-03931-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-03931-6