Abstract
In the framework of Granular Computing (GC), Interval type 2 Fuzzy Sets (IT2 FSs) play a prominent role by facilitating a better representation of uncertain linguistic information. Perceptual Computing (Per-C), a well-known computing with words (CWW) approach, and its various applications have nicely exploited this advantage. This paper reports a novel Per-C-based approach for student strategy evaluation. Examinations are generally oriented to test the subject knowledge of students. The number of questions that they are able to solve accurately judges success rates of students in the examinations. However, we feel that not only the solutions of questions, but also the strategy adopted for finding those solutions are equally important. More marks should be awarded to a student, who solves a question with a better strategy compared to a student, whose strategy is relatively not that good. Furthermore, the student’s strategy can be taken as a measure of his/her learning outcome as perceived by a faculty member. This can help to identify students, whose learning outcomes are not good, and, thus, can be provided with any relevant help, for improvement. The main contribution of this paper is to illustrate the use of CWW for student strategy evaluation and present a comparison of the recommendations generated by different CWW approaches. CWW provides us with two major advantages. First, it generates a numeric score for the overall evaluation of strategy adopted by a student in the examination. This enables comparison and ranking of the students based on their performances. Second, a linguistic evaluation describing the student strategy is also obtained from the system. Both these numeric score and linguistic recommendation are together used to assess the quality of a student’s strategy. Furthermore, the linguistic recommendation is useful for human beings as they naturally understand and express themselves using ‘words’, ‘words’ being treated as fuzzy information granules in the GC paradigm, which is perhaps the case with most of the human reasoning and concepts. In addition, through the comparison of the recommendations generated by different CWW approaches, we found that Per-C outperforms the others CWW approaches by generating unique recommendations in all the cases as well as modeling the word uncertainty in the best possible way.
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Notes
Human beings process linguistic information seamlessly due to the capability of human cognitive process.
The subjects are taught as a part of curriculum of a course like bachelors, masters, etc., that a student is enrolled in.
By solution methodology, we mean the collection of number of steps that form the solution of the question.
References
Aye MM, Thwin MMT (2008) Mobile agent based online examination system. IEEE Int Conf Electr Eng Electron Comput Telecommun Inf Technol 1:193–196 https://doi.org/10.1109/ECTICON.2008.4600405
Biswas R (1995) An application of fuzzy sets in students’ evaluation. J Fuzzy Sets Syst 74(2):187–194. https://doi.org/10.1016/0165-0114(95)00063-Q
Castillo O, Melin P (2008) Intelligent systems with interval type-2 fuzzy logic. Int J Innov Comput Inf Control 4(4):771–783
Castillo O, Cervantes L, Soria J, Sanchez M, Castro JR (2016a) A generalized type-2 fuzzy granular approach with applications to aerospace. Inf Sci 354:165–177
Castillo O, Amador-Angulo L, Castro JR, Garcia-Valdez M (2016b) A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf Sci 354:257–274. https://doi.org/10.1016/j.ins.2016.03.026
Cazarez-Castro N, Aguilar LT, Castillo O (2011) designing type-1 fuzzy logic controllers via fuzzy lyapunov synthesis for nonsmooth mechanical systems: the perturbed case. Comput Sist 14(3):283–293
Cazarez-Castro N, Aguilar LT, Castillo O (2012) Designing type-1 and type-2 fuzzy logic controllers via fuzzy Lyapunov synthesis for nonsmooth mechanical systems. Eng Appl Artif Intell 25(5):971–979. https://doi.org/10.1016/j.engappai.2012.03.003
Cervantes L, Castillo O (2015) Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control. Inf Sci 324:247–256. https://doi.org/10.1016/j.ins.2015.06.047
Chen S-M, Chen CD (2011) Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst Appl 38(4):3857–3864. https://doi.org/10.1016/j.eswa.2010.09.046
Chen S-M, Lee LW (2010a) Fuzzy multiple criteria hierarchical group decision-making based on interval type-2 fuzzy sets. IEEE Trans Syst Man Cybern Part A Syst Hum 40(5):1120–1128. https://doi.org/10.1109/TSMCA.2010.2044039
Chen S-M, Lee LW (2010b) Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Syst Appl 37(4):2790–2798. https://doi.org/10.1016/j.eswa.2009.09.012
Chen S-M, Lee SH, Lee CH (2001) A new method for generating fuzzy rules from numerical data for handling classification problems. Appl Artif Intell 15(7):645–664. https://doi.org/10.1080/088395101750363984
Chen S-M, Wang NY, Pan JS (2009) Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Syst Appl 36(8):11070–11076. https://doi.org/10.1016/j.eswa.2009.02.085
Chen SM, Lin TE, Lee LW (2014) Group decision making using incomplete fuzzy preference relations based on the additive consistency and the order consistency. Inf Sci 259:1–15. https://doi.org/10.1016/j.ins.2013.08.042
Greenfield S, John RI (2009) The uncertainty associated with a type-2 fuzzy set. Views Fuzzy Sets Syst Diff Perspect 243:471–483. https://doi.org/10.1007/978-3-540-93802-6_23
Gupta PK (2012) Mobile examination system. IEEE Int Conf Parallel Distrib Grid Comput https://doi.org/10.1109/PDGC.2012.6449836
Gupta PK, Madan M (2015) Per-C based student examination strategy evaluation in mobile evaluation system conducted through a smartphone. IEEE Int Conf Model Simul https://doi.org/10.1109/UKSim.2015.76
Gupta PK, Muhuri PK (2014) Perceptual computing based performance control mechanism for power efficiency in mobile embedded systems. IEEE Int Conf Fuzzy Syst https://doi.org/10.1109/FUZZ-IEEE.2014.6891865
Gupta PK, Muhuri PK (2016) Per-C based green computing model for handheld devices: an application of single person FOU. IEEE Int Conf Syst Man Cybern https://doi.org/10.1109/SMC.2016.7899161
Gupta MM, Ragade RK, Yager RR (1979) Advances in fuzzy set theory and applications. North-Holland Publishing Company, New York
Gupta PK, Madan M, Puri K, Gulati A (2014) Student oriented mobile based examination process. IEEE Int Conf Parallel Distrib Grid Comput. https://doi.org/10.1109/PDGC.2014.7030756
Hameed IA, Sorensen CG (2010) Fuzzy systems in education: a more reliable system for student evaluation. In: Fuzzy Systems. InTech, Croatia, pp 1–16. https://doi.org/10.5772/7216
Hao M, Mendel JM (2016) Encoding words into normal interval type-2 fuzzy sets: HM approach. IEEE Trans Fuzzy Syst 24(4):865–879. https://doi.org/10.1109/TFUZZ.2015.2486814
Herrera F, Martínez L (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 8(6):746–752. https://doi.org/10.1109/91.890332
Huapaya CR (2012) Proposal of fuzzy logic-based students’ learning assessment model. XVIII Congreso Argentino de Ciencias de la Computación, pp 1–10
Karnik NN, Mendel JM, Liang Q (1999) Type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 7(6):643–658. https://doi.org/10.1109/91.811231
Lin TY (1997) Granular Computing. Announcement of the BISC Special Interest Group on Granular Computing
Liu F, Mendel JM (2008) Encoding words into interval type-2 fuzzy sets using an interval approach. IEEE Trans Fuzzy Syst 16(6):1503–1521. https://doi.org/10.1109/TFUZZ.2008.2005002
Liu H, Gegov A, Cocea M (2016) Rule-based systems: a granular computing perspective. Granul Comput 1(4):259–274. https://doi.org/10.1007/s41066-016-0021-6
Mendel J (2003) Type-2 fuzzy sets: Some questions and answers. IEEE IEEE Neural Networks Society. http://sci2s.ugr.es/sites/default/files/files/linksInterest/Tutorials/Tutorial-Mendel-Type-2-Fuzzy-Sets.pdf. Accessed 8 June 2018
Mendel JM (2007) Computing with words: Zadeh, turing, popper and occam. IEEE Comput Intell Mag 2(4):10–17. https://doi.org/10.1109/MCI.2007.9066897
Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul Comput 1(1):59–69. https://doi.org/10.1007/s41066-015-0009-7
Mendel JM, Wu D (2008) Perceptual reasoning for perceptual computing. IEEE Trans Fuzzy Syst 16(6):1550–1564. https://doi.org/10.1109/TFUZZ.2008.2005691
Mendel J, Wu D (2010) Perceptual computing: aiding people in making subjective judgments. Wiley, Hoboken
Muhuri PK, Gupta PK, Mendel JM (2017) User satisfaction-aware power management in mobile devices based on perceptual computing. IEEE Trans Fuzzy Syst https://doi.org/10.1109/TFUZZ.2017.2773020
Muhuri PK, Ashraf Z, Lohani QD (2018) Multi-objective reliability-redundancy allocation problem with interval Type-2 Fuzzy uncertainty. IEEE Trans Fuzzy Syst 26(3):1339–1355. https://doi.org/10.1109/TFUZZ.2017.2722422
Pedrycz W (2001) Granular computing: an emerging paradigm. Springer Science & Business Media, New York
Pedrycz W, Chen S-M (2011) Granular computing and intelligent systems: design with information granules of higher order and higher type. Springer, Heidelberg
Pedrycz W, Chen S-M (2015a) Granular computing and decision-making: interactive and iterative approaches. Springer, Heidelberg
Pedrycz W, Chen S-M (2015b) Information granularity, big data, and computational intelligence. Springer, Heidelberg
Saleh I, Kim SI (2009) A fuzzy system for evaluating students’ learning achievement. J Expert Syst Appl 36(3):6236–6243. https://doi.org/10.1016/j.eswa.2008.07.088
Sanchez MA, Castillo O, Castro JR (2015a) Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. J Expert Syst Appl 42(14):5904–5914. https://doi.org/10.1016/j.eswa.2015.03.024
Sanchez MA, Castillo O, Castro JR (2015b) Information granule formation via the concept of uncertainty-based information with Interval Type-2 Fuzzy Sets representation and Takagi–Sugeno–Kang consequents optimized with Cuckoo search. J Appl Soft Comput 27:602–609. https://doi.org/10.1016/j.asoc.2014.05.036
Sevarac Z (2006) Neuro fuzzy reasoner for student modeling. IEEE Int Conf Adv Learn Technol. https://doi.org/10.1109/ICALT.2006.1652548
Sripan R, Suksawat B (2010) Propose of fuzzy logic-based students’ learning assessment. IEEE Int Conf Control Autom Syst https://doi.org/10.1109/ICCAS.2010.5669786
Wang HY, Chen S-M (2008) Evaluating students’ answerscripts using fuzzy numbers associated with degrees of confidence. IEEE Trans Fuzzy Syst 16(2):403–415. https://doi.org/10.1109/TFUZZ.2007.895958
Wu D, Mendel JM, Coupland S (2012) Enhanced interval approach for encoding words into interval type-2 fuzzy sets and its convergence analysis. IEEE Trans Fuzzy Syst 20(3):499–513. https://doi.org/10.1109/TFUZZ.2011.2177272
Yager RR (1999) Approximate reasoning as a basis for computing with words. In: Zadeh LA, Kacprzyk J (eds) Computing with words in information/intelligent systems 1. Studies in fuzziness and soft computing. Physica, Heidelberg, pp 50–77. https://doi.org/10.1007/978-3-7908-1873-4_3
Yao Y (2005) Perspectives of granular computing. IEEE Int Conf Granul Comput 1:85–90 https://doi.org/10.1109/GRC.2005.1547239
Yao Y (2016) A triarchic theory of granular computing. Granul Comput 1(2):145–157. https://doi.org/10.1007/s41066-015-0011-0
Zadeh LA (1965) Fuzzy sets. Inform Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inform Sci 8(3):199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111. https://doi.org/10.1109/91.493904
Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127. https://doi.org/10.1016/S0165-0114(97)00077-8
Zadeh LA (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput 2(1):23–25. https://doi.org/10.1007/s005000050030
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Gupta, P.K., Muhuri, P.K. Computing with words for student strategy evaluation in an examination. Granul. Comput. 4, 167–184 (2019). https://doi.org/10.1007/s41066-018-0109-2
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DOI: https://doi.org/10.1007/s41066-018-0109-2