Fuzzy Students’ Knowledge Modelling System through Revised Bloom’s Taxonomy
Keywords:Cognitive Processes Dimension, Fuzzy Logic, Knowledge Modelling System, Web-Based Educational System,
AbstractThe conveniences of web-based educational systems have attracted a large heterogeneous group of learners with various knowledge levels, learning goals, and others learning characteristics, to study online. To enhance the effectiveness of the web-based educational system in delivery knowledge, a system should be capable to identify the learners’ learning characteristics, and adapt the instructional process accordingly. Hence, this paper presented a students’ knowledge modelling system that is capable of infer and updating the students’ knowledge level in accordance to the cognitive processes dimension in the Revised Bloom’s Taxonomy. However, the students’ knowledge modeling process consists of tasks and factors that are vague and unmeasured, thus Fuzzy Logic is integrated into the students’ knowledge modeling system to deal with such uncertainties. The proposed fuzzy students’ knowledge modeling system uses fuzzy sets to represent students’ knowledge level and other influencing factors, and uses Mamdani type inference technique to determine and update knowledge levels.
“Distance learning,” Encyclopædia Britannica. 2016.
H. Henderson, Encyclopedia of Computer Science and Technology. New York: Infobase Publishing, 2009.
K. Chrysafiadi and M. Virvou, Advances in Personalized Web-Based Education, vol. 78. 2015.
A. Kavčič, A. Navia-Vázquez, and R. Pedraza-Jiménez, “Student modelling based on fuzzy inference mechanisms,” in Computer as a Tool. The IEEE Region 8 EUROCON 2003., 2003, vol. 2, pp. 379–383.
A. Kavcic, “Fuzzy User Modeling for adaptation in Educational Hypermedia,” IEEE Trans. Syst. Man Cybern. Part C (Applications Rev., vol. 34, no. 4, pp. 439–449, Nov. 2004.
D. R. Krathwohl, “A Revision of Bloom’s Taxonomy: An Overview,” Theory Pract., vol. 41, pp. 212–218, 2002.
B. S. Bloom, M. D. Engelhart, E. J. Furst, W. H. Hill, and D. R. Krathwohl, Eds., Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain. New York: David McKay.
C. Munzenmaier and N. Rubin, “Bloom’s Taxonomy: What’s Old Is New Again,” Perspectives (Montclair)., pp. 1–47, 2013.
L. W. Anderson, D. R. Krathwohl, P. W. Airasian, K. A. Cruikshank, R. E. Mayer, P. R. Pintrich, J. Raths, and M. C. Wittrock, Eds., A taxonomy for learning, teaching, and assessing: A revision of Bloom’s Taxonomy of Educational Objectives. New York: Longman, 2001.
P. Brusilovsky and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” in The Adaptive Web, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 3–53.
M. C. Polson and J. J. Richardson, Eds., Foundations of intelligent tutoring systems. Hillsdale: Lawrence Erlbaum Associates, 1988.
P. Brusilovsky, “The Construction and Application of Student Models in Intelligent Tutoring Systems,” J. Comput. Syst. Sci. Int., vol. 32, no. 1, pp. 70–89, 1994.
A. Grubišić, S. Stankov, and B. Žitko, “Stereotype Student Model for an Adaptive e-Learning System,” Int. J. Comput. Electr. Autom. Control Inf. Eng. , vol. 7, no. 4, pp. 440–447, 2013.
A. Weerasinghe and A. Mitrovic, “Facilitating Adaptive Tutorial Dialogues in EER-Tutor,” in Artificial Intelligence in Education, G. Biswas, S. Bull, J. Kay, and A. Mitrovic, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 630–631.
Z. Jeremić, J. Jovanović, and D. Gašević, “Student modeling and assessment in intelligent tutoring of software patterns,” Expert Syst. Appl., vol. 39, no. 1, pp. 210–222, 2012.
E. Millán and J. L. Pérez-de-la-Cruz, “A Bayesian Diagnostic Algorithm for student modeling and its evaluation,” User Model. Useradapt. Interact., vol. 12, no. 2/3, pp. 281–330, 2002.
L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, pp. 338–353, 1965.
C. Sammut and G. I. Webb, Eds., Encyclopedia of Machine Learning. Boston, MA: Springer US, 2010.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.