Performance Evaluation Using the Discrete Choquet Integral: Higher Education Sector
Abstract
Performance evaluation functions as an essential tool for decision
makers in the field of measuring and assessing the performance under the
multiple evaluation criteria aspect of the systems such as management, economy,
and education system. Besides, academic performance evaluation is one of the
critical issues in higher institution of learning. Even though the academic
evaluation criteria are inherently dependent, most of the traditional
evaluation methods take no account of the dependency. Currently, the discrete
Choquet integral can be proposed as a useful and effective aggregation operator
due to being capable of considering the interactions among the evaluation
criteria. In this paper, it is aimed to solve an academic performance
evaluation problem of students in a university in Turkey using the discrete
Choquet integral with the complexity-based method and the entropy-based method.
Moreover, the k-means method, which
has been widely used for evaluating students’ performance over 50 years, is
used to compare the effectiveness and the success of two different frameworks
based on discrete Choquet integral in the robustness check. Our results
indicate that the entropy-based Choquet integral outperforms the
complexity-based Choquet and k-means
method in most of the cases.
Keywords
References
- Angilella, S., Arcidiacono, S.G., Corrente, S,, Greco, S., & Matarazzo, B. (2017). An application of the SMAA–Choquet method to evaluate the performance of sailboats in offshore regattas. Operational Research, doi:10.1007/s12351-017-0340-7.
- Angilella, S., Corrente, S., & Greco, S. (2015). Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem. European Journal of Operational Research, 240(1), 172-182, doi: 10.1016/j.ejor.2014.06.031.
- Baker, R.S.J.D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
- Branke, J., Corrente, S., Greco, S., Słowiński, R., & Zielniewicz, P. (2016). Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. European Journal of Operational Research, 250(3), 884-901, doi: 10.1016/j.ejor.2015.10.027.
- Calvo, T., Mayor, G., & Mesiar, R. (2002). Aggregation operators: New trends and applications. Physica-Verlag, Heidelberg, New York.
- Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. Kodratoff, Y. (Eds) Machine Learning Lecture Notes in Computer Science, Springer, Berlin, Heidelberg.
- Chang, H.J., Liu, H.C., Tseng, S.W., & Chang, F.M. (2009). A comparison on Choquet integral with different information-based fuzzy measures. In Proceedings of the 8th International Conference on Machine Learning and Cybernetics (pp. 3161-3166).
- Chmielewski, M.R., & Grzymala-Busse, J.W. (1996). Global discretization of continuous attributes as preprocessing for machine learning. International Journal of Approximate Reasoning, 15(4), 319-331. doi: 10.1016/S0888-613X(96)00074-6.
Details
Primary Language
English
Subjects
Studies on Education
Journal Section
Research Article
Publication Date
March 21, 2019
Submission Date
November 14, 2018
Acceptance Date
March 6, 2019
Published in Issue
Year 2019 Volume: 6 Number: 1