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Teaching performance evaluation by means of a hierarchical multifactorial evaluation model based on type-2 fuzzy sets

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Abstract

Teaching performance evaluation plays an important role in the management of education institutions. In this process, the relevant factors may be complicated and various, it is difficult to dealt with the factors once for all, and a decomposed smaller part sometimes could be easily handled. Based on this consideration, a hierarchical multifactorial evaluation model based on type-2 fuzzy sets is formulated and applied to evaluate teaching performance. In the constructed model, in order to obtain a more rational evaluation result, the factor weights are replaced by triangular fuzzy numbers, and the defuzzification results are represented by the triangular incentre points. To estimate the factor and sub-factor weights in the multifactorial evaluation, a new method of fuzzy analytic hierarchy process (AHP) based on triangular incentre point is developed, and the effectiveness of the hierarchical multifactorial evaluation model is verified with an specific instance.

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References

  1. Nikolaidis Y, Dimitriadis SG (2014) On the student evaluation of university courses and faculty members’ teaching performance. Eur J Oper Res 238:199–207

    Article  MathSciNet  MATH  Google Scholar 

  2. Kuzmanovic M, Savic G, Andric GB, et al. (2013) A Conjoint-based approach to student evaluations of teaching performance. Expert Syst Appl 40:4083–4089

    Article  Google Scholar 

  3. Başaran MA, Kalayc N, Tark AM (2011) A novel hybrid method for better evaluation: evaluating university instructors teaching performance by combining conventional content analysis with fuzzy rule based systems. Expert Syst Appl 38:12565–12568

    Article  Google Scholar 

  4. Chen J, Hsieh H, Do Q (2015) Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Appl Soft Comput 28:100–108

    Article  Google Scholar 

  5. Li H, Wang P, Yen V (1998) Factor spaces theory and its applications to fuzzy information processing(1): the basics of factor spaces. Fuzzy Sets Syst 95(2):147–160

    Article  MathSciNet  MATH  Google Scholar 

  6. Zhou L, Sun K, Li H (2016) Multifactorial decision making based on type-2 fuzzy sets and factor space approach. J Intell Fuzzy Syst 30:2257–2266

    Article  Google Scholar 

  7. Feng S, Xu L (1999) Decision support for fuzzy comprehensive evaluation of urban development. Fuzzy Sets Syst 105:1–12

    Article  Google Scholar 

  8. Peidro D, Mula J, Poler R, Verdegay J (2012) Fuzzy optimization for supply chain planning under supply demand and process uncertainties. Fuzzy Sets Syst 160:2640–2657

    Article  MathSciNet  MATH  Google Scholar 

  9. Zhang X, Deng Y, Chan F, Mahadevan S (2015) A fuzzy extended analytic network process-based approach for global supplier selection. Appl Intell 43:760–772

    Article  Google Scholar 

  10. Wei X, Luo X (2015) Online comment-based hotel quality automatic assessment using improved fuzzy comprehensive evaluation and fuzzy cognitive map. IEEE Trans Fuzzy Syst 23:72–84

    Article  Google Scholar 

  11. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MathSciNet  MATH  Google Scholar 

  12. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8:199–249

    Article  MathSciNet  MATH  Google Scholar 

  13. Wu D, Mendel J (2007) Aggregation using the linguistic weighted average and interval type-2 fuzzy sets. IEEE Trans Fuzzy Syst 15:1145–1161

    Article  Google Scholar 

  14. Zhou SM, Chiclana F, John RI, Garibaldi JM (2008) Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers. Fuzzy Sets Syst 159(24):3281–3296

    Article  MathSciNet  MATH  Google Scholar 

  15. Mendel JM (2014) Type-2 fuzzy logic systems made simple general: a tutorial. IEEE Trans Fuzzy Syst 22(5):1162–1182

    Article  Google Scholar 

  16. Zhai D, Mendel J (2012) Comment on toward general type-2 fuzzy logic systems based on z-slices. IEEE Trans Fuzzy Syst 20(5):996–997

    Article  Google Scholar 

  17. Ganjefar S, Solgi Y (2015) A Lyapunov stable type-2 fuzzy wavelet network controller design for a bilateral teleoperation system. Inf Sci 311:1–17

    Article  MathSciNet  Google Scholar 

  18. Zhou SM, Garibaldi JM, John RI, Chiclana F (2009) On constructing parsimonious type-2 fuzzy logic systems via influential rule selection. IEEE Trans Fuzzy Syst 17(3):654–667

    Article  Google Scholar 

  19. Zhou SM, John RI, Chiclana F, Garibaldi JM (2010) On aggregating uncertain information by type-2 OWA operators for soft decision making. Int J Intell Syst 25(6):540–558

    MATH  Google Scholar 

  20. Vaidya SK (2006) Analytic hierarchy process: an overview of applications. Eur J Oper Res 169:1–29

    Article  MathSciNet  MATH  Google Scholar 

  21. Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95:649–655

    Article  MATH  Google Scholar 

  22. Peidro D, Mula J, Poler R, Verdegay JL (2012) Fuzzy optimization for supply chain planning under supply demand and process uncertainties. Fuzzy Sets Systems 160:2640–2657

    Article  MathSciNet  MATH  Google Scholar 

  23. Tadic D, Gumus A, Arsovski S, et al. (2013) An evaluation of quality goals by using fuzzy AHP and fuzzy TOPSIS methodology. J Intell Fuzzy Syst 25:547–556

    MathSciNet  Google Scholar 

  24. Liou T, Wang M (1992) Ranking fuzzy numbers with integral value. Fuzzy Sets Syst 50:247–255

    Article  MathSciNet  MATH  Google Scholar 

  25. Cheng CH (1998) A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets Syst 95:307–317

    Article  MathSciNet  MATH  Google Scholar 

  26. Rouhparvar H, Panahi A (2015) A new definition for defuzzification of generalized fuzzy numbers and its application. Appl Soft Comput 30:577–584

    Article  Google Scholar 

  27. Li H, Wang P, Yen V (2000) Factor spaces theory in fuzzy information processing-composition of states of factors and multifactorial decision making. Computers and Mathematics with Applications 39:245–265

    Article  MathSciNet  MATH  Google Scholar 

  28. Saaty TL, Begicevic N (2010) The scope of human values and human activities in decision making. Appl Soft Comput 10:963–974

    Article  Google Scholar 

  29. Kahraman C, Ertay T, Buyukozkan G (2006) A fuzzy optimization model for QFD planning process using analytic network approach. Eur J Oper Res 171:390–411

    Article  MATH  Google Scholar 

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos.: 61473327, 61374118).

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Authors and Affiliations

  1. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Lintao Zhou, Hongxing Li & Kaibiao Sun

Authors
  1. Lintao Zhou
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  2. Hongxing Li
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  3. Kaibiao Sun
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Correspondence to Lintao Zhou.

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The authors declare that there is no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Zhou, L., Li, H. & Sun, K. Teaching performance evaluation by means of a hierarchical multifactorial evaluation model based on type-2 fuzzy sets. Appl Intell 46, 34–44 (2017). https://doi.org/10.1007/s10489-016-0816-9

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  • DOI: https://doi.org/10.1007/s10489-016-0816-9

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