Evaluating the Efficiency of Higher Secondary Education State Boards in India: A DEA-ANN Approach

  • Natthan Singh
  • Millie Pant
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


This study proposes the integration of two nonparametric methodologies - Data Envelopment Analysis and Artificial Neural Network for efficiency evaluation. The paper initially outlines the research work conducted in the education sector using DEA and ANN. Furthermore, the case study for the paper is conducted on various State Boards (which are used as DMU’s) in Indian Higher Secondary Education System for efficiency evaluation using DEA which is integrated with soft computing technique ANN in order to increase discriminatory power, ranking and future prediction. The above two methods are compared on their practical use as a performance measurement tool on a set of Indian State Boards in Indian Higher Secondary Education System with multiple inputs and outputs criteria. The results demonstrate that ANN-DEA Integration optimizes the performance and increases the discriminatory power and ranking of the decision making units.


Data Envelopment Analysis Artificial Neural Network Indian Higher Secondary Education 


  1. 1.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30(9), 1078–1092 (1984)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bradley, S., Johnes, G., Millington, J.: The effect of competition on the efficiency of secondary schools in England. Eur. J. Oper. Res. 135(3), 545–568 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Afonso, A., St Aubyn, M.: Non-parametric approaches to education and health efficiency in OECD countries. J. Appl. Econ. 8(2), 227–246 (2005)Google Scholar
  5. 5.
    Johnes, J., Li, Y.U.: Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Econ. Rev. 19(4), 679–696 (2008)CrossRefGoogle Scholar
  6. 6.
    Kuah, C.T., Wong, K.Y.: Efficiency assessment of universities through data envelopment analysis. Procedia Comput. Sci. 3, 499–506 (2011)CrossRefGoogle Scholar
  7. 7.
    Agasisti, T.: The efficiency of Italian secondary schools and the potential role of competition: a data envelopment analysis using OECD-PISA2006 data. Educ., Econ. Taylor Francis J. 21(5), 520–544 (2013)Google Scholar
  8. 8.
    Selim, S., Bursalıoğlu, S.A.: Efficiency of higher education in turkey: a bootstrapped two-stage DEA approach. Int. J. Stat. Appl. 5(2), 55–67 (2015)Google Scholar
  9. 9.
    Sagarra, M., et al.: Exploring the efficiency of Mexican universities: integrating data envelopment analysis and multidimensional scaling. Omega 63, 123–133 (2017)CrossRefGoogle Scholar
  10. 10.
    Athanassopoulos, A.D., Curram, S.P.: A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. J. Oper. Res. Soc. 47(8), 1000–1016 (1996)CrossRefzbMATHGoogle Scholar
  11. 11.
    Emrouznejad, A., Shale, E.: A combined neural network and DEA for measuring efficiency of large scale datasets. Comput. Ind. Eng. 56(1), 249–254 (2009)CrossRefGoogle Scholar
  12. 12.
    Liu, H.-H., Chen, T.-Y., Chiu, Y.-H., Kuo, F.-H.: A comparison of three-stage DEA and artificial neural network on the operational efficiency of semi-conductor firms in Taiwan. Mod. Econ. 4(1), 20–31 (2013)CrossRefGoogle Scholar
  13. 13.
    Kuo, R.J., Wang, Y.C., Tien, F.C.: Integration of artificial neural network and MADA methods for green supplier selection. J. Clean. Prod. 18(12), 1161–1170 (2010)CrossRefGoogle Scholar
  14. 14.
    Kwon, H.: Performance modeling of mobile phone providers: a DEA-ANN combined approach. Benchmarking Int. J. 22(6), 1120–1144 (2014)CrossRefGoogle Scholar
  15. 15.
    Oladokun, V.O., Adebanjo, A.T., Charles-Owaba, O.E.: Predicting students’ academic performance using artificial neural network: a case study of an engineering course. Pac. J. Sci. Technol. 9(1), 72–79 (2008)Google Scholar
  16. 16.
    Chen, C., et al.: Online 24-h solar power forecasting based on weather type classification using artificial neural network. Sol. Energy 85(11), 2856–2870 (2011)CrossRefGoogle Scholar
  17. 17.
    Cheh, J.J., Weinberg, R.S., Yook, K.C.: An application of an artificial neural network investment system to predict takeover targets. J. Appl. Bus. Res. (JABR) 15(4), 33–46 (2013)CrossRefGoogle Scholar
  18. 18.
    Chhachhiya, D., Sharma, A., Gupta, M.: Designing optimal architecture of neural network with particle swarm optimization techniques specifically for educational dataset. In: 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, pp 52–57. IEEE, Noida (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations