NEUTROSOPHIC ASSESSMENT OF STUDENT MATHEMATICAL SKILLS
DOI:
https://doi.org/10.31110/2413-1571-2023-038-2-004Keywords:
Fuzzy Assessment Methods, Grey Numbers (GNs), Grade Point Average (GPA) Index, Neutrosophic Sets (NSs), Neutrosophic TripletsAbstract
Formulation of the problem. Assessment is an important component of the teaching process, because it helps the instructor to determine the student mistakes and to improve their performance by adapting suitably his/her teaching methods. In this work we investigate the problem of evaluating the student overall performance, when the teacher is not sure about the accuracy of the grades assigned to them. This happens, either because the teacher had not enough time to assess properly the students’ mathematical skills, or because, in case of a written examination/test, some students did not present clearly or did not justified properly their answers.
Materials and methods. Fuzzy assessment methods using neutrosophic sets and grey numbers, as well as the calculation of the Grade Point Average (GPA) index are used in this work for the assessment of a student class mean performance, quality performance, and overall performance when the teacher has doubts about the grades assigned to some students.
Results. The paper focuses on a classroom application designed for the assessment, with qualitative (linguistic) grades, of mathematical skills of the engineering students of two Departments of the School of Engineering of the Graduate Technological Educational Institute (TEI) of Western Greece (University of Peloponnese) being at their first term of studies. The instructor was the same person for both Departments. The teaching methodology for the first Department (experimental group) involved a combined use of computers and classroom lectures, whereas for the second Department (control group) involved only lectures in the classical way on the board.
Conclusions. The use of neutrosophic sets provides a useful tool for evaluating the student overall performance when the teacher has doubts about the accuracy of the grades assigned to them. The outcomes of the classroom application demonstrated a superiority of the experimental group. This superiority, however, was significant with respect to its mean and overall performance (neutrosophic assessment), but rather negligible with respect to its quality performance. This gives a strong indication that the use of computers in the teaching process helps more the mediocre and weak students and not so much the good students.
Downloads
References
Atanassov, K.T. (1986), Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, 20(1),.87-96.
Deng, J. (1982), Control Problems of Grey Systems, Systems and Control Letters, 288-294.
Dubois, D. & Prade, H. (2001), Possibility theory, probability theory and multiple-valued logics: A clarification, Ann. Math. Artif. Intell., 32, 35-66.
Klir, G. J. & Folger, T. A. (1988), Fuzzy Sets, Uncertainty and Information, Prentice-Hall, London.
Kosko, B. (1990), Fuzziness Vs. Probability, Int. J. of General Systems, 17(2-3) , 211-240.
Moore, R.A., Kearfort, R.B., Clood, M.G. (1995), Introduction to Interval Analysis, 2nd ed., SIAM, Philadelphia, PA, USA.
Smarandache, F. (1998), Neutrosophy / Neutrosophic probability, set, and logic, Proquest, Michigan, USA.
Smarandache, F. (2016), Subtraction and Division of Neutrosophic Numbers, Critical Review, Vol. XIII, 103-110.
Smarandache, F. (2021), Indeterminacy in Neutrosophic Theories and their Applications, International Journal of Neutrosophic Science, 15(2), 89-97.
Voskoglou, M.Gr. (2017), Finite Markov Chain and Fuzzy Logic Assessment Models: Emerging Research and Opportunities, Create space independent publishing platform (Amazon), Columbia, SC.
Voskoglou, M.Gr. (2019a), Assessing Human-Machine Performance under Fuzzy Conditions, Mathematics, 7, article 230.
Voskoglou, M.Gr. (2019b), Fuzzy Systems, Extensions and Relative Theories, WSEAS Transactions on Advances in Engineering Education, 16, 63-69.
Wang, H., Smarandanche, F., Zhang, Y. & Sunderraman, R. (2010), Single Valued Neutrosophic Sets, Review of the Air Force Academy (Brasov), 1(16), 10-14, 2010.
Zadeh, L.A. (1965), Fuzzy Sets, Information and Control, 8, 338-353.
Zadeh, L.A. (1973), Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern., 3, 28–44.
Zadeh, L.A. (1978), Fuzzy Sets as a basis for a theory of possibility, Fuzzy Sets Syst., 3–28.
Downloads
Published
Issue
Section
Categories
How to Cite
License
Copyright (c) 2023 Майкл Воскоглоу

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Authors grant the journal a right of the first publication of the work under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)that allows others freely to use (read, copy and print) submissions, search content and link to published articles, disseminate their full text and use them for any legitimate non-commercial purposes (i.e. educational or scientific) with the mandatory reference to the article’s authors and initial publication in this journal.
- Original published articles cannot be used by users (exept authors) for commercial purposes or distributed by third-party intermediary organizations for a fee.

