A Heterogeneous Linguistic MAGDM Framework to Classroom Teaching Quality Evaluation
 
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1
Central South University, Hunan, Changsha, China
2
Central South University, Changsha, China
3
School of Business, Central South University, Changsha, China
CORRESPONDING AUTHOR
Hongyu Zhang
School of Business, Central South University, Changsha, China
Online publish date: 2017-08-04
Publish date: 2017-08-04
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):4929–4956
KEYWORDS
ABSTRACT
Focusing on multi-attribute group decision making (MAGDM) regarding classroom teaching quality evaluation, this article aims to devise a novel evaluation framework based on heterogeneous linguistic information. In this framework, a four-level evaluation process of classroom teaching quality is established. Then, the weights of the sub-attributes are estimated objectively by integrating a newly proposed score function of interval linguistic 2-tuples and optimization models which consider the realistic situation that alternatives are not equally weighted. Subsequently, the exploitation process is implemented by two branches: taking the possibility measurement to rank teachers with respect to different attributes and extending the technique for order preference by similarity to ideal solution (TOPSIS) method to assess the overall performance of teachers. Finally, a simulated case is furnished to illustrate how to apply the presented framework to realistic classroom teaching quality evaluation problems. Hopefully, this work would be beneficial to the improvement of classroom teaching quality.
 
REFERENCES (50)
1.
Bordogna, G., Fedrizzi, M., & Passi, G. (1997). A linguistic modeling of consensus in group decision making based on OWA operator. IEEE Transactions on Systems, Man, and Cybernetics-Part A, 27, 126-132.
 
2.
Chang, T. C., & Wang, H. (2016). A multi criteria group decision-making model for teacher evaluation in higher education based on cloud model and decision tree. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1243-1262.
 
3.
Chen, J. F., Hsieh, H. N., & Do, Q. H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing, 28, 100-108.
 
4.
Dong, P., & Dai, F. (2009). Evaluation for teaching quality based on fuzzy neural network. Proceedings of the First International Workshop on Education Technology and Computer Science, 112-115.
 
5.
Dong, Y. C., & Herrera-Viedma, E. (2015). Consistency-driven automatic methodology to set interval numerical scales of 2-tuple linguistic term sets and its use in the linguistic GDM with preference relation, 45(4), 780-792.
 
6.
Dong, Y. C., Li, C. C., & Herrera, F. (2016). Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesitant unbalanced linguistic information. Information Sciences, 367-368, 259-278.
 
7.
Dong, Y. C., Li, C. C., Xu, Y. F., & Gu, X. (2015). Consensus-based group decision making under multi-granular unbalanced 2-tuple linguistic preference relations. Group Decision and Negotiation, 24, 217-242.
 
8.
Dong, Y. C., Xu, Y. F., & Yu, S. (2009). Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Transactions on Fuzzy Systems, 17, 1366-1378.
 
9.
Dong, Y. C., Zhang, G. Q., Hong, W. C., & Yu, S. (2013). Linguistic computational model based on 2-tuples and intervals. IEEE Transactions on Fuzzy Systems, 21(6), 1006-1018.
 
10.
Dong, Y. C., Zhang, H. J., & Herrera-Viedma, E. (2016). Consensus reaching model in the complex and dynamic MAGDM problem. Knowledge-Based Systems, 106, 206-219.
 
11.
Düğenci, M. (2016). A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information. Applied Soft Computing, 41, 120-134.
 
12.
Dutta, B., & Guha, D. (2015). Partitioned Bonferroni mean based on linguistic 2-tuple for dealing with multi-attribute group decision making. Applied Soft Computing, 37, 166-179.
 
13.
He, X. N., Zhu, Z. Q., Zhou, Y., Lu, G. Y., & Liu, Q. Y. (2010). University teaching quality evaluation using fuzzy comprehensive evaluation approach. Proceedings of the Second International Workshop on Education Technology and Computer Science, 1, 616-619.
 
14.
Herrera, F., & Martínez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8, 746-752.
 
15.
Kim, S. H., & Han, C. H. (1999). An interactive procedure for multi-attribute group decision making with incomplete information. Computers and Operations Research, 26, 755-772.
 
16.
Kim, S. H., Choi, S. H., & Kim, J. K. (1999). An interactive procedure for multiple attribute group decision making with incomplete information: Range-based approach. European Journal of Operational Research, 118(1), 139-152.
 
17.
Levrat, E., Voisin, A., Bombardier, S., & Bremont, J. (1997). Subjective evaluation of car seat comfort with fuzzy techniques. International Journal of Intelligent Systems, 12, 891-913.
 
18.
Li, J., & Wang, J. Q. (2017). Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cognitive Computation, DOI: 10.1007/s12559-017-9476-2.
 
19.
Liu, P., & Yu, X. (2014). 2-Dimension uncertain linguistic power generalized weighted aggregation operator and its application in multiple attribute group decision making. Knowledge-Based Systems, 57, 69-87.
 
20.
Liu, S., Chan, F. T. S., & Ran, W. X. (2013). Multi-attribute group decision-making with multi-granularity linguistic assessment information: An improved approach based on deviation and TOPSIS. Applied Mathematical Modelling, 37(24), 10129-10140.
 
21.
Martínez, L., & Herrera, F. (2012). An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges. Information Sciences, 207, 1-18.
 
22.
Nguyen, H. (2015). A new knowledge-based measure for intuitionistic fuzzy sets and its application in multiple attribute group decision making. Expert Systems with Applications, 42(22), 8766-8774.
 
23.
Nie, R. X., Wang, J. Q., & Li, L. (2017). A shareholder voting method for proxy advisory firm selection based on 2-tuple linguistic picture preference relation. Applied Soft Computing, DOI: 10.1016/j.asoc.2017.06.055.
 
24.
Onu, P. U., Quan, X., Xu, L., Orji, J., & Onu, E. (2017). Evaluation of sustainable acid rain control options utilizing a fuzzy TOPSIS multi-criteria decision analysis model frame work. Journal of Cleaner Production, 141, 612-625.
 
25.
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.
 
26.
Opricovic, S. & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514-529.
 
27.
Rodríguez, R.M., Labella, A., & Martínez, L. (2016). An overview on fuzzy modelling of complex linguistic preferences in decision making. International Journal of Computational Intelligence Systems, 9, 81-94.
 
28.
Sahin, R., & Liu, P. (2015). Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information. Neural Computing & Applications, 1-13.
 
29.
Tai, W. S., & Chen, C. T. (2009). A new evaluation model for intellectual capital based on computing with linguistic variable. Expert Systems with Applications, 36, 3483-3488.
 
30.
Wan, S. P., & Dong, J. Y. (2015). Power geometric operators of trapezoidal intuitionistic fuzzy numbers and application to multi-attribute group decision making. Applied Soft Computing, 29, 152-168.
 
31.
Wang, J., Wang, J. Q., Zhang, H. Y., & Chen, X. H. (2017). Distance-based multi-criteria group decision-making approaches with multi-hesitant fuzzy linguistic information. International Journal of Information Technology & Decision Making, 16(4), 1069-1099.
 
32.
Wang, Z. J., Li, K. W., & Wang, W. (2009). An approach to multiattribute decision making with interval-valued intuitionistic fuzzy assessments and incomplete weights. Information Sciences, 179, 3026-2040.
 
33.
Wei, G. W. (2010). Extension of TOPSIS method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Knowledge-Based Systems, 25, 623-634.
 
34.
Wei, G.W. (2010). GRA method for multiple attribute decision making with incomplete weight information in intuitionistic fuzzy setting. Knowledge-Based Systems, 23, 243-247.
 
35.
Xu, Z. (2004). Uncertain Multiple Attribute Decision Making: Methods and Applications, Qinghua University, China.
 
36.
Xu, Z. (2005). Deviation measures of linguistic preference relations in group decision making. Omega, 33, 249-254.
 
37.
Xu, Z., & Chen, J. (2007). An interactive method for fuzzy multiple attribute group decision making. Information Sciences, 177, 248-263.
 
38.
Xue, Y. X., You, J. X., Lai, X. D., & Liu, H. C. (2016). An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Applied Soft Computing, 38, 703-713.
 
39.
Yu, S. M., Wang, J., & Wang, J. Q. (2016). An extended TODIM approach with intuitionistic linguistic numbers. International Transactions in Operational Research, DOI: 10.1111/itor.12363.
 
40.
Zhang, H. J., Dong, Y. C., & Chen, X. (2017). The 2-rank consensus reaching model in the multi-granular linguistic multiple attribute group decision making. IEEE Transactions on Systems, Man and Cybernetics: Systems, DOI: 10.1109/TSMC.2017.2694429.
 
41.
Zhang, H. M. (2012). The multiattribute group decision making method based on aggregation operators with interval-valued 2-tuple linguistic information. Mathematical and Computer Modelling, 56, 27-35.
 
42.
Zhang, H. M. (2013). Some interval-valued 2-tuple linguistic aggregation operators and application in multiattribute group decision making. Applied Mathematical Modelling, 37, 4269-4282.
 
43.
Zhang, H. Y., Peng, H. G., Wang, J., & Wang, J. Q. (2017). An extended outranking approach for multi-criteria decision-making problems with linguistic intuitionistic fuzzy numbers. Applied Soft Computing, 59, 462-474.
 
44.
Zhang, S., Zhu, J., Liu, X., & Chen, (2016). Y. Regret theory-based group decision-making with multidimensional preference and incomplete weight information. Information Fusion, 31, 1-13.
 
45.
Zhang, W., Xu, Y., & Wang, H. (2015). A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. International Journal of System Sciences, 47(2), 389-405.
 
46.
Zhang, X. Y., & Wang, J. Q. (2017). Consensus-based framework to MCGDM under multi-granular uncertain linguistic environment. Journal of Intelligent & Fuzzy Systems, 33(2), 1263-1274.
 
47.
Zhang, X., & Xu, Z. (2015). Soft computing based on maximizing consensus and fuzzy TOPSIS approach to interval-valued intuitionistic fuzzy group decision making. Applied Soft Computing, 26, 42-56.
 
48.
Zhang, Z., & Guo, C. H. (2012). A method for multi-granularity uncertain linguist group decision making with incomplete weight information. Knowledge-Based Systems, 26, 111-119.
 
49.
Zhou, H., Wang, J. Q., & Zhang, H. Y. (2016). Stochastic multi-criteria decision-making approach based on SMAA-ELECTRE with extended grey numbers. International Transactions in Operational Research, DOI: 10.1111/itor.12380.
 
50.
Zyoud, S. H., Kaufmann, L. G., Shaheen, H., Samban, S., & Fuchs-Hanusch, D. (2016). A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS. Expert Systems with Applications, 61, 86-105.
 
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