Abstract
We present a model made up of linguistic multi-criteria decision making processes to integrate the answers to heterogeneous questionnaires, based on a five-point Likert scale, into a unique form rooted in the widespread course experience questionnaire. The main advantage of having the resulting integrated questionnaire is that it can be incorporated into other course experience questionnaire surveys to make benchmarking among organizations. This model has been applied to integrate heterogeneous educational questionnaires at the University of Granada.
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References
C. McInnis, Studies of Student Life: an overview, European Journal of Education 39(4) (2004) 383–394.
G. D. Kuh, The National Survey of Student Engagement: Conceptual Framework and Overview of Psychometric Properties (2003) Indiana University Center for Postsecondary Research and Planning, http://nsse.iub.edu/pdf/conceptual_framework_2003.pdf.
P. Ramsden and N. J. Entwistle, Effects of Academic Departments on Students’ Approaches to Studying. British Journal of Educational Psychology 51 (1981) 368–383.
P. Ramsden, E. Martin and J. Bowden, School environment and sixth form pupil’s approaches to learning. British Journal of Educational Psychology 59 (1989) 129–142.
C. McInnis, P. Griffin, R. James and H. Coates, Development of the Course Experience (DETYA, Canberra, 2001).
K. Wilson, A. Lizzio and P. Ramsden, The Development, Validation and Application of the Course Experience Questionnaire. Studies in Higher Education 1(1997) 33– 53.
J. A. Gliem, R. R. Gliem, Calculating, Interpreting, and Reporting Cronbach’s Alpha Reliability Coefficient for Likert-Type Scales, Midwest Research to Practice Conference in Adult, Continuing, and Community Education, (The Ohio State University, Columbus, 2003), pp. 82–88.
R. Likert. A technique for the measurement of attitudes. Archives of Psychology (Columbia University Press, New York, 1931).
J. P. McIver and E. G. Carmines, Unidimensional scaling (Sage, Newbury Park, CA, 1981).
W. Deng and W. Pei, Fuzzy neural based importance-performance analysis for determining critical service attributes. Expert Syst. Appl. 36(2) (2009) 3774–3784.
H. K. Chiou and G. H. Tzeng and D. C. Cheng, Evaluating sustainable fishing development strategies using fuzzy MCDM approach. Omega 33 (2005) 223– 234.
L. A. Zadeh, Fuzzy sets. Information and Control 8 (1965) 338–353.
L. A. Zadeh, The concept of a linguistic variable and its applications to approximate reasoning, Inf. Sci. 8(pt. I, II) (1975) 199–249 and 301–357.
F. Chiclana, F. Herrera and E. Herrera-Viedma, Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations, Fuzzy Sets and Systems 97 (1998) 33–48.
F. Herrera, E. Herrera-Viedma and L. Martinez, A fusion approach for managing multi-granularity linguistic term sets in decision making, Fuzzy Sets and Systems 114 (2000) 43–58.
F. Herrera and E. Herrera-Viedma, Linguistic decision analysis: Steps for solving decision problems under linguistic information, Fuzzy Sets and Systems 115(10) (2000) 67–82.
G. Bordogna and G. Passi, A fuzzy linguistic approach generalizing Boolean information retrieval: A model and its evaluation, J. Amer. Soc. Inf. Sci. 44 (1993) 70–82.
E. Herrera-Viedma, A. G. López-Herrera, A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling. International Journal of Computational Intelligence Systems 3(4) (2010) 420–437
E. Herrera-Viedma, An information retrieval system with ordinal linguistic weighted queries based on two weighting elements, Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 9 (2001) 77–88.
E. Herrera-Viedma, A. G. López-Herrera, M. Luque and C. Porcel, A fuzzy linguistic IRS model based on a 2-tuple fuzzy linguistic approach, Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 15 (2007) 225–250.
M. Delgado, J. L. Verdegay and M. A. Vila, Linguistic decision making models, Int. J. Intell. Syst. 7 (1992) 479– 492.
F. Mata, L. Martínez, E. Herrera-Viedma, An Adaptive Consensus Support Model for Group Decision Making Problems in a Multi-Granular Fuzzy Linguistic Context. IEEE Transactions on Fuzzy Systems 17(2) (2009) 279–290.
J. L. Garcia-Lapresta, B. Llamazares and M. Martinez-Panero. A Social Choice Analysis of the Borda Rule in a General Linguistic Framework, International Journal of Computational Intelligence Systems 3(4) (2010) 501– 513.
P. P. Bonissone and K. S. Decker, Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity, Uncertainty in Artificial Intelligence, eds. L. H. Kanal and J. F. Lemmer, (North-Holland, Amsterdam, 1986), pp. 217–247.
P. P. Bonissone, A fuzzy sets based linguistic approach: Theory and applications, Approximate Reasoning in Decision Analysis eds. M. M. Gupta and E. Sanchez (North-Holland, Amsterdam, 1982), pp. 329–339.
F. Herrera, E. Herrera-Viedma, and J. L. Verdegay, A sequential selection process in group decision making with linguistic assessment, Inf. Sci. 85 (1995) 223–239.
R. R. Yager, An approach to ordinal decision making, Int. J. Approx. Reas. 12 (1995) 237–261.
B. Bouchon-Meunier and J. Yao, Linguistic Modifiers and imprecise categories, Int. J. Intell. Syst. 7 (1992) 25– 36.
M. Delgado, J. L. Verdegay and M. A. Vila, On aggregation operations of linguistic labels, Int. J. Intell. Syst. 8 (1993) 351–370.
F. Herrera, E. Herrera-Viedma and J. L. Verdegay, Direct approach processes in group decision making using linguistic OWA operators, Fuzzy Sets and Systems 79 (1996) 175–190.
F. Herrera and E. Herrera-Viedma, Aggregation operators for linguistic weighted information, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 27(5) (1997) 646–656.
R. R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man and Cybernetics 18(1) (1988) 183–190.
J. Kacprzyk, Group decision making with a fuzzy linguistic majority, Fuzzy Sets Systems 18(2) (1986) 105– 118.
L. A. Zadeh, A computational approach to fuzzy quantifiers in natural languages, Computer & Mathematics with Applications 9(1) (1983) 149–184.
F. Herrera, E. Herrera-Viedma and J.L. Verdegay, Choice processes for non-homogeneous group decision making in linguistic setting, Fuzzy Sets and Systems 94 (1997) 287–308.
F. Herrera, E. Herrera-Viedma and J.L. Verdegay, A rational consensus model in group decision making using linguistic assessments, Fuzzy Sets and Systems 88 (1997) 31–49.
G. Bordogna, M. Fedrizzi and G. Passi, A linguistic modelling of consensus in group decision making based on OWA operators, IEEE Trans. Systems Man Cybernet. 27 (1997) 126–132.
M. Fedrizzi and L. Mich, Rule based model for consensus reaching group decisions support, Proc. 3rd Conf. on Information Processing and Management of Uncertainty, (Palma de Mallorca, 1992), pp. 301–304.
R. R. Yager, Non-numeric multi-criteria multi-person decision making, Group Decision Negotiation 2 (1993) 81–93.
S. A. Orlovsky, Decision making with a fuzzy preference relation, Fuzzy Sets and Systems 1 (1978) 155–167.
M. Roubens, Some properties of choice functions based on valued binary relations, European Journal of Operational Research 40 (1989) 309–321.
F. Herrera and E. Herrera-Viedma, Choice Functions and Mechanisms for Linguistic Preference Relations, European Journal of Operational Research 120 (2000) 144–161.
R. R. Yager, Quantifier guided aggregation using OWA operators, Int. J. Intell. Syst. 11(1) (1996) 49–73.
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Carrasco, R.A., Villar, P., Hornos, M.J. et al. A Linguistic Multi-Criteria Decision Making Model Applied to the Integration of Education Questionnaires. Int J Comput Intell Syst 4, 946–959 (2011). https://doi.org/10.2991/ijcis.2011.4.5.19
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DOI: https://doi.org/10.2991/ijcis.2011.4.5.19