Use of Soft sets and the Bloom's Taxonomy for Assessing Learning Skills
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
1 - Department of Mathematical Sciences, Graduate Technological Educational Institute of Western Greece, Meg. Alexandrou 1, 263 34 Patras, Greece.
کلید واژه: fuzzy sets, Learning, Soft sets, Blooms taxonomy, Assessment methods,
چکیده مقاله :
Learning, a universal process that all individuals experience, is a fundamental component of human cognition. It combines cognitive, emotional and environmental influences for acquiring or enhancing ones knowledge and skills. Volumes of research have been written about learning and many theories have been developed for the description of its mechanisms. The goal was to understand objectively how people learn and then develop teaching approaches accordingly. In this paper soft sets, a generalization of fuzzy sets introduced in 1999 by D. Molodstov as a new mathematical tool for dealing with the uncertainty in a parametric manner, are used for assessing student learning skills with the help of the Blooms taxonomy. Blooms taxonomy has been applied and is still applied by generations of teachers as a teaching tool to help balance assessment by ensuring that all orders of thinking are exercised in student learning. The innovative assessment method introduced in this paper is very useful when the assessment has qualitative rather than quantitative characteristics. A classroom application is also presented illustrating its applicability under real conditions.
Learning, a universal process that all individuals experience, is a fundamental component of human cognition. It combines cognitive, emotional and environmental influences for acquiring or enhancing ones knowledge and skills. Volumes of research have been written about learning and many theories have been developed for the description of its mechanisms. The goal was to understand objectively how people learn and then develop teaching approaches accordingly. In this paper soft sets, a generalization of fuzzy sets introduced in 1999 by D. Molodstov as a new mathematical tool for dealing with the uncertainty in a parametric manner, are used for assessing student learning skills with the help of the Blooms taxonomy. Blooms taxonomy has been applied and is still applied by generations of teachers as a teaching tool to help balance assessment by ensuring that all orders of thinking are exercised in student learning. The innovative assessment method introduced in this paper is very useful when the assessment has qualitative rather than quantitative characteristics. A classroom application is also presented illustrating its applicability under real conditions.
[1] L. W. Anderson and D. R. Krathwohl, A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives, Boston, Allyn and Bacon, (2000).
[2] B. S. Bloom, M. D. Engelhart, E. J. Furst, W. H. Hill and D. R. Krathwohl, Taxonomy of educational objectives: The classi cation of educational goals, Handbook I: Cognitive domain, New York, David McKay Company, (1956).
[3] K. Cherry, History and Key Concepts of Behavioral Psychology, (2019). Available online: https://www.verywellmind.com/behavioral-psychology-4157183.
[4] K. Crawford, Vygotskian approaches in human development in the information era, Educ. Stud. Math. 31 (1996), 43-62.
[5] G. Siemens, Connectivism: A learning theory for the digital age, International Journal of Instructional Technology and Distance Learning, 2(1) (2005). Available online: http://www.itdl.org/Journal/Jan05/article01.htm.
[6] B. Fairbanks, 5 educational learning theories and how to apply them (2021). Available online:
https://www.phoenix.edu/blog/educational-learning-theories.
[7] A. Kharal and B. Ahmad, Mappings on Soft Classes, New Mathematics and Natural Computation, 7(3) (2011), 471-481.
[8] B. Kosko, Fuzzy Thinking: The New Science of Fuzzy Logic, NY, Hyperion, (1993).
[9] J. McKinley, Critical argument and writer identity: Social constructivism as a theoretical framework for EFL academic writing, Crit. Inq. Lang. Stud., 12 (2015), 184-207.
[10] D. Molodtsov, Soft Set Theory-First Results, Computers and Mathematics with Applications, 37 (4-5) (1999), 19-31.
[11] A. P. Paplinski, Neuro-Fuzzy Computing, Lecture Notes, Monash University, Australia, (2005).
[12] M. Shabir and M. Naz, On Soft Topological Spaces, Computers and Mathematics with Applications, 61 (2011), 1786-1799.
[13] K. S. Taber, Constructivism as educational theory: Contingency in learning and optimally guided instruction, in Hassaskhah, J. (Ed.), Educational Theory, Hauppauge, NY, Nova Science Publishers, (2011), 39-61.
[14] B. K. Tripathy and K. R. Arun, Soft Sets and Its Applications, in J. S. Jacob (Ed.), Handbook of Research on Generalized and Hybrid Set Structures and Applications for Soft Computing, Hersey, PA, IGI Global, (2016), 65-85.
[15] M. Gr. Voskoglou, Finite Markov Chain and Fuzzy Logic Assessment Models: Emerging Research and Opportunities, Columbia, SC, Createspace.com Amazon, (2017).
[16] M. Gr. Voskoglou, Assessing Human-Machine Performance under Fuzzy Conditions, Mathematics, 7 (2019).
[17] M. Gr. Voskoglou, Generalizations of Fuzzy Sets and Related Theories, in M. Voskoglou (Ed.), An Essential Guide to Fuzzy Systems, N. Y., Nova Publishers, (2019), 345-352.
[18] M. Gr. Voskoglou, Application of Soft Sets to Assessment Processes, American Journal of Applied Mathematics and Statistics, 10(1) (2022), 1-3.
[19] B. Wallace, A. Ross, J. B. Davies and T. Anderson, The Mind, the Body and the World: Psychology after Cognitivism, Upton Pyne, UK, Imprint Academic, (2007).
[20] L. A. Zadeh, Fuzzy Sets, Information and Control, 8 (1965), 338-353.
