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Fuzzy Evaluation of Examinees Through Multiple Choice Questions
Volume 28, Issue 4 (2017), pp. 609–628
Pub. online: 1 January 2017      Type: Research Article      Open accessOpen Access
Received
1 February 2017
Accepted
1 September 2017
Published
1 January 2017

Abstract

Fuzzy sets can be used in many old-fashioned aspects of our lives in order to reach better performance and make fairer judgments. Evaluation through examination is typically conducted by educational centers, and multiple choice question (MCQ) exams are widely applied to score the examinees. Since scoring is potentially a difficult process to judge, we propose to evaluate examinees by fuzzy evaluation method. This method can overcome the main shortcoming of the classical MCQs, i.e. the random selection of the choices. The evaluation of the proposed fuzzy MCQ is more accurate and its ranking of examinees is fairer than classical MCQ.

References

 
Akbay, R., Sahiner, A., Yılmaz, N. (2016). Determining of the achievement of students by using classical and modern optimization techniques. Eurasian Journal of Physics & Chemistry Education, 8, 3–13.
 
Amo-Salas, M., Arroyo-Jimenez, M.M., Bustos-Escribano, D., Fairén-Jiménez, E., López-Fidalgo, J. (2014). New indices for refining multiple choice questions. Journal of Probability and Statistics, 2014, 1–8.
 
Arora, N., Saini, J.R. (2016). Estimation and approximation using neuro-fuzzy systems. International Journal of Intelligent Systems and Applications, 8, 9–18.
 
Baba, A.F., Melis Cin, F., Ordukaya, E. (2015). Intelligent fuzzy assessment system for English Academic writing in engineering education. International Journal of Engineering Education, 31, 83–93.
 
Bai, S.M., Chen, S.M. (2008a). Evaluating Students’ Learning Achievement using Fuzzy Membership Functions and Fuzzy Rules. Expert Systems with Applications, 34, 399–410.
 
Bai, S.M., Chen, S.M. (2008b). Automatically constructing grade membership functions and fuzzy rules for students’ evaluation. Expert Systems with Applications, 35, 1408–1414.
 
Biswas, R. (1995). An application of fuzzy sets in students’ evaluation. Fuzzy Sets and Systems, 74, 187–194.
 
Cabrerizo, F.J., Chiclana, F., Al-Hmouz, R., Morfeq, A.S., Balamash, E., Herrera-Viedma, E. (2015). Fuzzy decision making and consensus: challenges. Journal of Intelligent & Fuzzy Systems, 29(3), 1109–1118.
 
Chai, K.C., Tay, K.M., Lim, C.P. (2015). A new fuzzy peer assessment methodology for cooperative learning of students. Applied Soft Computing, 32, 468–480.
 
Chang, D.F., Sun, C.M. (1993). Fuzzy assessment of learning performance of junior high school students. In: First National Symposium on Fuzzy Theory and Application, Hsinchu, Taiwan, Republic of China, pp. 10–15.
 
Chen, S.M., Lee, C.H. (1999). New methods for students’ evaluation using fuzzy sets. Fuzzy Sets and Systems, 104, 209–218.
 
Chen, S.M., Wang, S. (2009). Evaluating students’ answerscripts based on interval-valued fuzzy grade sheets. Expert Systems with Applications, 36, 9839–9846.
 
Chen, S.M., Li, T.K. (2011). Evaluating students’ learning achievement based on fuzzy rules with fuzzy reasoning capability. Expert Systems with Applications, 38, 4368–4381.
 
Chiang, T.T., Lin, C.M. (1994). Application of fuzzy theory to teaching assessment. In: Second National Symposium on Fuzzy Theory and Application, Hsinchu, Taiwan, Republic of China, pp. 92–97.
 
Chiclana, F., Herrera, F., Herrera-Viedma, E. (2001). Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations. Fuzzy Sets and Systems, 122, 277–291.
 
Chu, F. (1990). Quantitative evaluation of university teaching quality—an application of fuzzy set and approximate reasoning. Fuzzy Sets and Systems, 37, 1–11.
 
Daly, A.J. (2012). Data, dyads, and dynamics: exploring data use and social networks in educational improvement. Teachers College Record, 114, 1–38.
 
Darwish, S.M. (2016). Uncertain measurement for student performance evaluation based on selection of boosted fuzzy rules. IET Science, Measurement & Technology, 8, 213–219.
 
Fahim, M., Dehghankar, A. (2014). Towards fuzzy scores in language multiple-choice tests. International Journal of Language Learning and Applied Linguistics World, 6, 291–308.
 
Fahmi, A., Kahraman, C. (2015). May the success be a matter of degree? Fuzzy examination and scoring through multiple choice questions. In: 27th European Conference on Operational Research, Glasgow, UK, p. 185.
 
Hameed, I.A. (2016). A fuzzy system to automatically evaluate and improve fairness of multiple-choice questions (MCQs) based exams. In: 8th International Conference on Computer Supported Education, Rome, Italy, pp. 476–481.
 
Hameed, I.A., Elhoushy, M., Zalam, B.A., Osen, O.L. (2016). An interval type-2 fuzzy logic system for assessment of students’ answer scripts under high levels of uncertainty. In: 8th International Conference on Computer Supported Education, Rome, Italy, pp. 40–48.
 
Herrera, F., Alonso, S., Chiclana, F., Herrera-Viedma, E. (2009). Computing with words in decision making: foundations, trends and prospects. Fuzzy Optimization and Decision Making, 8, 337–364.
 
Hristova, M., Sotirova, E. (2008). Generalized net model of the multifactor method of teaching quality estimation at universities. In: 4th International IEEE Conference on Intelligent Systems, pp. 16–20.
 
Ibrahim, S., Kim, S. (2009). A fuzzy system for evaluating students’ learning achievement. Expert Systems with Applications, 36, 6236–6243.
 
Johanyak, Z.C., Kovacs, L. (2014). Fuzzy evaluation of student assignment sheets. Gradus, 1, 130–136.
 
Kakani, B., Dalal, D., Dabhi, A. (2016). Improved solution on students answer sheet assessment using fuzzy rules. In: IEEE Conference on Advances in Signal Processing, Rome, Italy, pp. 435–439.
 
Law, C.K. (1996). Using fuzzy numbers in educational grading system. Fuzzy Sets and Systems, 83, 311–323.
 
Li, X., Chen, K., Ruan, J., Shi, C. (2015). A fuzzy TOPSIS for assessing higher vocational education development levels in uncertainty environments. Journal of Intelligent & Fuzzy Systems, 31, 3083–3093.
 
Liu, Y. (2015). Research on the foreign language teaching effectiveness evaluation with intuitionistic fuzzy information. Journal of Intelligent & Fuzzy Systems, 28, 787–793.
 
Ma, J., Zhou, D. (2000). Fuzzy sets approach to the assessment of student-centered learning. IEEE Transactions on Education, 43, 237–241.
 
Mandinach, E.B., Honey, M., Light, D. (2006). A theoretical framework for data-driven decision making. In: Annual Meeting of the American Educational Research Association, San Francisco, CA.
 
Morente-Molinera, J.A., Perez, I.J., Urena, M.R., Herrera-Viedma, E. (2015). Systematic decision making: an extended multi-criteria decision making model. On Multi-Granular Fuzzy Linguistic Modelling in Group Decision Making Problems: A Systematic. Review and Future Trends, 74, 49–60.
 
Othman, M. (2016). Fuzzy evaluation method using fuzzy rule approach in multicriteria analysis. Yugoslav Journal of Operations Research, 18, 95–107.
 
Parmar, V.P., Kumbharana, C.K. (2015). Enhancement of online computerized examination by incorporating artificial intelligence to one word answer questions along with multiple choice questions. International Journal of Computer Applications, 132, 25–28.
 
Pejic, A., Stanic, P.M., Pletl, S., Kiss, B. (2013). Fuzzy multicriteria analysis for student project evaluation. In: 11th International IEEE Symposium on Intelligent Systems and Informatics, pp. 409413.
 
Rodriguez, R.M., Martinez, L., Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20, 109–119.
 
Rodriguez, R.M., Martinez, L., Torra, L.V., Xu, Z., Herrera, F. (2014). Hesitant fuzzy sets: state of the art and future directions. International Journal of Intelligent Systems, 29, 495–524.
 
Romero, C., Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40, 601–618.
 
Romero, C., Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3, 12–27.
 
Sakthivel, E., Senthamarai Kannan, K., Arumugam, S. (2013). Optimized evaluation of students performances using fuzzy logic. International Journal of Scientific & Engineering Research, 4, 1128–1133.
 
Salunkhe, S.S., Joshi, Y., Deshpande, A. (2016). Can fuzzy relational calculus bring complex issues in selection of examiners into focus? Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 25, 85–97.
 
Shahbazova, S., Kosheleva, O. (2014). Fuzzy multiple-choice quizzes and how to grade them. Journal of Uncertain Systems, 8, 216–221.
 
Urena, M.R., Chiclana, F., Morente-Molineraz, E., Herrera-Viedma, E. (2015). Fuzzy decision making and consensus: challenges. Information Sciences, 302(1), 14–32.
 
Wang, H.Y., Chen, S.M. (2006). New methods for evaluating the answerscripts of students using fuzzy sets. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Annecy, France, pp. 442–451.
 
Wang, H.Y., Chen, S.M. (2008). Evaluating students’ answerscripts using fuzzy numbers associated with degrees of confidence. IEEE Transaction on Fuzzy Systems, 16, 403–415.
 
Wayman, J.C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed at Risk, 10, 295–308.
 
Wohlstetter, P., Datnow, A., Park, V. (2001). Creating a system for data-driven decision-making: applying the principal-agent framework. School Effectiveness and School Improvement, 19, 239–259.
 
Xu, Z.S. (2007). Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control and Decision, 22, 215–219.
 
Yannibelli, V., Armentano, M., Berdun, F., Amandi, A. (2016). A steady-state evolutionary algorithm for building collaborative learning teams in educational environments considering the understanding levels and interest levels of the students. Journal of Universal Computer Science, 22, 1298–1318.
 
Yu, X.H., Xu, Z.H., Hu, J.Q., Liu, S.S. (2017). Systematic decision making: An extended multi-criteria decision making model. Technological and Economic Development of Economy, 23(1), 157–177.
 
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8, 338–353.
 
Zeng, S., Palacios-Marqués, D., Zhu, F. (2016). A new model for interactive group decision making with intuitionistic fuzzy preference relations. Informatica, 27(4), 911–928.
 
Zupanc, K., Bosnic, Z. (2015). Advances in the field of automated essay evaluation. Informatica, 39, 383–395.

Biographies

Fahmi Ali
fahmi@itu.edu.tr

A. Fahmi holds a bachelor’s degree in industrial engineering from University of Tabriz, Iran. He graduated from the master’s program in management engineering from Istanbul Technical University, Turkey. His research interests focus on data analytics, fuzzy sets, and intelligent decision making. He has contributed to various research projects related to socio-economic, medical, and psychology contexts.

Kahraman Cengiz
kahramanc@itu.edu.tr

C. Kahraman is a full professor at Istanbul Technical University (ITU). His research areas are engineering economics, quality control and management, statistical decision making, multicriteria decision making, and fuzzy decision making. He published about 200 journal papers and about 150 conference papers. He became the guest editors of many international journals and the editor of many international books from Springer and Atlantis Press. He is the member of editorial boards of 20 international journals. He was the vice dean of ITU Management Faculty between 2004–2007 and the head of ITU Industrial Engineering Department between 2010–2013.


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Keywords
evaluation methodologies multiple choice questions fuzzy sets fuzzy examination fuzzy multiple choice questions

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