Fuzzy Students’ Knowledge Modelling System through Revised Bloom’s Taxonomy

Authors

  • W. T. Ng Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • C. S. Teh Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.

Keywords:

Cognitive Processes Dimension, Fuzzy Logic, Knowledge Modelling System, Web-Based Educational System,

Abstract

The 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.

References

“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.

Downloads

Published

2017-09-15

How to Cite

Ng, W. T., & Teh, C. S. (2017). Fuzzy Students’ Knowledge Modelling System through Revised Bloom’s Taxonomy. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-9), 169–174. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2693