Skip to main content
Top

2017 | OriginalPaper | Chapter

Interval Type-2 Fuzzy Logic Systems for Evaluating Students’ Academic Performance

Authors : Ibahim A. Hameed, Mohanad Elhoushy, Ottar L. Osen

Published in: Computers Supported Education

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Assessment method sends messages to students to define and priorities what is important to learn and ultimately how they spend their time leaning it. Traditional grading methods are largely based on human judgments, which tend to be subjective. In addition, it is based on sharp criteria instead of fuzzy criteria and suffers from erroneous scores assigned by indifferent or inexperienced examiners, which represent a rich source of uncertainties, which might impair the credibility of the system. In an attempt to reduce uncertainties and provide more objective, reliable, and precise grading, a sophisticated assessment approach based on type-2 fuzzy set theory is developed. In this paper, interval type-2 (IT2) fuzzy sets, which are a special case of the general T2 fuzzy sets, are used. The transparency and capabilities of type-2 fuzzy sets in handling uncertainties is expected to provide an evaluation system able to justify and raise the quality and consistency of assessment judgments. A simplified implementation of interval type-2 fuzzy system using the basic knowledge of type-1 fuzzy is presented. A comparison between the use of type-1, interval type-2 fuzzy systems and the simplified IT2 fuzzy systems in reducing uncertainties and providing more transparent and fair assessment that can reflect needs of individual students and foster development is presented.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Hameed, I.A., Elhoushy, M., Zalam, B.A., Osen, O.L.: An interval type-2 fuzzy logic system for assessment of students’ answer scripts under high levels of uncertainty. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016), Rome, Italy, vol. 2, pp. 40–48 (2016) Hameed, I.A., Elhoushy, M., Zalam, B.A., Osen, O.L.: An interval type-2 fuzzy logic system for assessment of students’ answer scripts under high levels of uncertainty. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016), Rome, Italy, vol. 2, pp. 40–48 (2016)
2.
go back to reference Boud, D.: Developing Student Autonomy in Learning, 2nd edn. Kogan Page, London (1988) Boud, D.: Developing Student Autonomy in Learning, 2nd edn. Kogan Page, London (1988)
3.
go back to reference Saleh, I., Kim, S.-I.: A fuzzy system for evaluating students’ learning achievement. Expert Syst. Appl. 36(3), 6236–6243 (2009)CrossRef Saleh, I., Kim, S.-I.: A fuzzy system for evaluating students’ learning achievement. Expert Syst. Appl. 36(3), 6236–6243 (2009)CrossRef
4.
go back to reference Hameed, I.A., Sørensen, C.G.: Fuzzy systems in education: a more reliable system for student evaluation. In: Azar, A.T. (ed.) Fuzzy Systems, INTECH, Croatia (2010). ISBN: 978-953-7619-92-3 Hameed, I.A., Sørensen, C.G.: Fuzzy systems in education: a more reliable system for student evaluation. In: Azar, A.T. (ed.) Fuzzy Systems, INTECH, Croatia (2010). ISBN: 978-953-7619-92-3
5.
go back to reference Zadeh, L.A.: Towards a generalized theory of uncertainity (GTU) - an outline. Inf. Sci. 172, 1–40 (2005)CrossRefMATH Zadeh, L.A.: Towards a generalized theory of uncertainity (GTU) - an outline. Inf. Sci. 172, 1–40 (2005)CrossRefMATH
6.
go back to reference Daradkeh, M., McKinnon, A., Churcher, C.: Supporting informed decision-making under uncertainty and risk through interactive visualization. Aust. Comput. Sci. Commun. 35(5), 23–32 (2013) Daradkeh, M., McKinnon, A., Churcher, C.: Supporting informed decision-making under uncertainty and risk through interactive visualization. Aust. Comput. Sci. Commun. 35(5), 23–32 (2013)
7.
go back to reference Biswas, R.: An application of fuzzy sets in students’ evaluation. Fuzzy Sets Syst. 74(2), 187–194 (1995)CrossRefMATH Biswas, R.: An application of fuzzy sets in students’ evaluation. Fuzzy Sets Syst. 74(2), 187–194 (1995)CrossRefMATH
8.
go back to reference Echauz, J.R., Vachtsevanos, G.J.: Fuzzy grading system. IEEE Trans. Educ. 38(2), 158–165 (1995)CrossRef Echauz, J.R., Vachtsevanos, G.J.: Fuzzy grading system. IEEE Trans. Educ. 38(2), 158–165 (1995)CrossRef
9.
go back to reference Law, C.K.: Using fuzzy numbers in education grading system. Fuzzy Sets Syst. 83(3), 311–323 (1996)CrossRef Law, C.K.: Using fuzzy numbers in education grading system. Fuzzy Sets Syst. 83(3), 311–323 (1996)CrossRef
10.
go back to reference Wilson, E., Karr, C.L., Freeman, L.M.: Flexible, adaptive, automatic fuzzy-based grade assigning system. In: Proceedings of the North American Fuzzy Information Processing Society Conference, pp. 334–338 (1998) Wilson, E., Karr, C.L., Freeman, L.M.: Flexible, adaptive, automatic fuzzy-based grade assigning system. In: Proceedings of the North American Fuzzy Information Processing Society Conference, pp. 334–338 (1998)
11.
go back to reference Chen, S.M., Lee, C.H.: New methods for students’ evaluating using fuzzy sets. Fuzzy Sets Syst. 104(2), 209–218 (1999)CrossRef Chen, S.M., Lee, C.H.: New methods for students’ evaluating using fuzzy sets. Fuzzy Sets Syst. 104(2), 209–218 (1999)CrossRef
12.
go back to reference Ma, J., Zhou, D.: Fuzzy set approach to the assessment of student-centered learning. IEEE Trans. Educ. 43(2), 237–241 (2000)CrossRef Ma, J., Zhou, D.: Fuzzy set approach to the assessment of student-centered learning. IEEE Trans. Educ. 43(2), 237–241 (2000)CrossRef
13.
go back to reference Weon, S., Kim, J.: Learning achievement evaluation strategy using fuzzy membership function. In: Proceedings of the 31st ASEE/IEEE Frontiers in Education Conference, Reno, NV (2001) Weon, S., Kim, J.: Learning achievement evaluation strategy using fuzzy membership function. In: Proceedings of the 31st ASEE/IEEE Frontiers in Education Conference, Reno, NV (2001)
14.
go back to reference Wang, H.Y., Chen, S.M.: Evaluating students’ answerscripts using fuzzy numbers associated with degrees of confidence. Proc. IEEE Trans. Fuzzy Syst. 16(2), 403–415 (2008)CrossRef Wang, H.Y., Chen, S.M.: Evaluating students’ answerscripts using fuzzy numbers associated with degrees of confidence. Proc. IEEE Trans. Fuzzy Syst. 16(2), 403–415 (2008)CrossRef
15.
go back to reference Bai, S.-M., Chen, S.-M.: Automatically constructing grade membership functions of fuzzy rules for students’ evaluation. Expert Syst. Appl. 35(3), 1408–1414 (2008)CrossRef Bai, S.-M., Chen, S.-M.: Automatically constructing grade membership functions of fuzzy rules for students’ evaluation. Expert Syst. Appl. 35(3), 1408–1414 (2008)CrossRef
16.
go back to reference Bai, S.-M., Chen, S.-M.: Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules. Expert Syst. Appl. 34, 399–410 (2008)CrossRef Bai, S.-M., Chen, S.-M.: Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules. Expert Syst. Appl. 34, 399–410 (2008)CrossRef
17.
go back to reference Hameed, I.A.: Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems. Expert Syst. Appl. 38(6), 7135–7142 (2011)CrossRef Hameed, I.A.: Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems. Expert Syst. Appl. 38(6), 7135–7142 (2011)CrossRef
18.
go back to reference Hameed, I.A.: A fuzzy system to automatically evaluate and improve fairness of multiple-choice questions (MCQs) based exams. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016), Rome, Italy, vol. 1, pp. 476–481 (2016) Hameed, I.A.: A fuzzy system to automatically evaluate and improve fairness of multiple-choice questions (MCQs) based exams. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016), Rome, Italy, vol. 1, pp. 476–481 (2016)
19.
go back to reference Hameed, I.A.: A simplified implementation of interval type-2 fuzzy system and its application in students’ academic evaluation. In: Proceedings of the IEEE World Congress on Computational Intelligence, Vancouver, Canada (2016) Hameed, I.A.: A simplified implementation of interval type-2 fuzzy system and its application in students’ academic evaluation. In: Proceedings of the IEEE World Congress on Computational Intelligence, Vancouver, Canada (2016)
20.
go back to reference Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)CrossRef Liang, Q., Mendel, J.M.: Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)CrossRef
22.
go back to reference Iancu, I.: A mamdani type fuzzy logic controller. In: Dadios, E. (ed.) Fuzzy Logic - Controls, Concepts, Theories and Applications (2012). ISBN: 978-953-51-0396-7, InTech Iancu, I.: A mamdani type fuzzy logic controller. In: Dadios, E. (ed.) Fuzzy Logic - Controls, Concepts, Theories and Applications (2012). ISBN: 978-953-51-0396-7, InTech
23.
go back to reference Zhao, J., Bose, B.K.: Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: 28th IEEE Annual Conference of the Industrial Electronics Society, pp. 229–234. IEEE Press, New York (2002) Zhao, J., Bose, B.K.: Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: 28th IEEE Annual Conference of the Industrial Electronics Society, pp. 229–234. IEEE Press, New York (2002)
24.
go back to reference Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3(1), 28–44 (1973)MathSciNetCrossRefMATH Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3(1), 28–44 (1973)MathSciNetCrossRefMATH
25.
go back to reference King, P., Mamdani, E.: The application of fuzzy control to industrial process. Automatica 13, 235–242 (1997)CrossRef King, P., Mamdani, E.: The application of fuzzy control to industrial process. Automatica 13, 235–242 (1997)CrossRef
26.
go back to reference Mendel, J.M.: Rule-based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)MATH Mendel, J.M.: Rule-based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)MATH
27.
28.
go back to reference Mendel, J.M., Liang, Q.: Pictorial comparison of type-1 and type-2 fuzzy logic systems. In: Proceedings of IASTED International Conference on Intelligent Systems and Control (1999) Mendel, J.M., Liang, Q.: Pictorial comparison of type-1 and type-2 fuzzy logic systems. In: Proceedings of IASTED International Conference on Intelligent Systems and Control (1999)
29.
go back to reference Wu, D., Tan, W.W.: A simplified architecture for type-2 FLSs and its application to nonlinear control. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 485–490, (2004) Wu, D., Tan, W.W.: A simplified architecture for type-2 FLSs and its application to nonlinear control. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 485–490, (2004)
31.
go back to reference Sepulveda, R., Castillo, O., Melin, P., Rodriguez-Diaz, A., Montiel, O.: Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf. Sci. 177, 2023–2048 (2007)CrossRef Sepulveda, R., Castillo, O., Melin, P., Rodriguez-Diaz, A., Montiel, O.: Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic. Inf. Sci. 177, 2023–2048 (2007)CrossRef
32.
go back to reference Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems. Prentice-Hall, Upper Saddle River (2001). 07458MATH Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems. Prentice-Hall, Upper Saddle River (2001). 07458MATH
33.
go back to reference Wu, D.: Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons. IEEE Trans. Fuzzy Syst. 21(1), 80–99 (2013)CrossRef Wu, D.: Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons. IEEE Trans. Fuzzy Syst. 21(1), 80–99 (2013)CrossRef
34.
go back to reference Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)CrossRef Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)CrossRef
35.
go back to reference Mamdani, E.M., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7, 1–13 (1975)CrossRefMATH Mamdani, E.M., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7, 1–13 (1975)CrossRefMATH
Metadata
Title
Interval Type-2 Fuzzy Logic Systems for Evaluating Students’ Academic Performance
Authors
Ibahim A. Hameed
Mohanad Elhoushy
Ottar L. Osen
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-63184-4_22

Premium Partner