A Fuzzy Logic Decision Making Of Student Performance Using Minimization of Weighted Regret Method
J. Betty jane1, E.N. Ganesh2

1J.Bettyjane, Department of computer science and Engineering, vels University, Chennai, India,
2Dr. E.N. Ganesh, Dean, School of Engineering, Vels University, Chennai, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1042-1046 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8312088619/2019©BEIESP | DOI: 10.35940/ijeat.F8312.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The method of reasoning which is similar to that of human reasoning is known as fuzzy logic. In this competitive world, in the field of education, more data has been generated and kept as records for the evaluation of student’s performance. With these large databases of records of the student, the experts would feel difficult to make a decision, since they cannot judge or decide a student understanding just by the paper test because some students could not write well but they are good in solving problems practically. So, there are some uncertainties that are found in evaluating a student’s performance. Here in this paper, we have proposed a model for making a decision from the uncertain data to find in which part of this academic skills the students is low, whether in practical or written or in workshop. Hence to make a decision we need to evaluate the students separately for their practical competence, written test and other academic skills. Then with the help of the expert opinion we will be able to decide in which part the students are low in competence. We have proposed a fuzzy decision making approach called Minimization of regret method(MMR) with the Weighted Average (OWA) operator known as MWR approach for making a decision to find in which part of the attribute the students score low marks and give them training and improve their ability in that particular area of skills such as practical or written or in workshop.
Keywords: Fuzzy logic method, fuzzification., fuzzy sets, OWA operator.