Design and Development of Topic-based Students’ Knowledge Modelling System using Fuzzy Set Theory and Visual Analytics

  • Authors

    • Wen Thing Ng
    • Chee Siong Teh
    • Chwen Jen Chen
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23347
  • Fuzzy set theory, Knowledge modelling system, Topic-based student modelling system, Visual analytics.

  • Abstract

    To the field of education, advances in technologies raise opportunities and challenges in delivering personalised learning to heterogeneous group of learners while monitoring and analysing their learning progress and learning outcomes. This paper addresses the challenges of gaining a big picture about learners’ knowledge that is essential for providing personalised learning. Next, a system that models students’ knowledge is proposed in which topic-based approach, fuzzy set theory, and visual analytics were applied to design and develop the proposed system.  The proposed system was designed with the capabilities to model, analyse and report individual learner’s knowledge, whereas its applicability was presented and discussed based on a real-world case study.

     

     

  • References

    1. [1] Spector J (2018), Future trends of designing learning in the global context. Authentic Learning Through Advances in Technologies, Chang TW, Huang R, and Kinshuk, Eds. Springer, Singapore, pp. 205–216.

      [2] Brusilovsky P (2016), Domain modeling for personalized guidance. Design Recommendations for Intelligent Tutoring Systems: Volume 4 - Domain Modeling, Sottilare R, Graesser A, Hu X, Olney A, Nye B, and Sinatra A, Eds. Orlando, FL: U.S. Army Research Laboratory, pp. 165–183.

      [3] Ng WT & Teh CS (2017), Fuzzy students’ knowledge modelling system through Revised Bloom’ s Taxonomy. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), Vol. 9, No. 2–9, pp. 169–174.

      [4] Shute VJ & Zapata-Rivera D (2012), Adaptive educational systems. Adaptive technologies for training and education, Durlach PJ & Lesgold A, Eds. Cambridge: Cambridge University Press, pp. 7–27.

      [5] Bates S (2014), Personalised learning: Implications for curricula, staff and students. Paper presented at the Universitas 21 (U21) Educational Innovation Conference, The University of New South Wales (UNSW), Australia.

      [6] Shute VJ (2006), Tensions, trends, tools, and technologies: Time for an educational sea change. ETS Research Report Series, Vol. 2006, No. 1, pp. i-49.

      [7] Woolf BP, Ed. (2010), A roadmap for education technology [National Science Foundation Grant #0637190]. Washington: Computing Community Consortium.

      [8] Volarić T, Vasić D & Brajković E (2017), Adaptive tool for teaching programming using conceptual maps. Advanced technologies, systems, and applications, Hadžikadić M & Avdaković S, Eds. Switzerland: Springer International Publishing, pp. 335–347.

      [9] Polson MC & Richardson JJ (1988), Foundations of intelligent tutoring systems. New Jersey: Lawrence Erlbaum Associates.

      [10] Chrysafiadi K & Virvou M (2015), Advances in personalized web-based education, Vol. 78. Cham: Springer International Publishing.

      [11] Jeremić Z, Jovanović J & Gašević D (2012), Student modeling and assessment in intelligent tutoring of software patterns. Expert Systems with Applications, Vol. 39, No. 1, pp. 210–222.

      [12] Grubišić A, Stankov S & Žitko B (2013), Stereotype student model for an adaptive e-learning system. International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 7, No. 4, pp. 440–447.

      [13] Zadeh LA (1965), Fuzzy sets. Information and Control, Vol. 8, pp. 338–353.

      [14] Ruipérez-Valiente JA, Muñoz-Merino PJ, Leony D & Delgado Kloos C (2015), ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform. Computers in Human Behavior, Vol. 47, pp. 139–148.

      [15] Brusilovsky P & Millán E (2007), User models for adaptive hypermedia and adaptive educational systems. The Adaptive Web, Brusilovsky P, Kobsa A & Nejdl W, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 3–53.

      [16] Chrysafiadi K & Virvou M (2013), Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, Vol. 40, No. 11, pp. 4715–4729.

      [17] Stansfield JL, Carr BP & Goldstein IP (1976), Wumpus advisor I: A first implementation of a program that tutors logical and probabilistic reasoning skills. Cambridge, Massachusetts: Massachusetts Institute of Technology.

      [18] Martins AC, Faria L, De Carvalho CV & Carrapatoso E (2008), User modeling in adaptive hypermedia educational systems. Journal of Educational Technology & Society, Vol. 11, No. 1, pp. 194–207.

      [19] Sosnovsky S & Brusilovsky P (2015), Evaluation of topic-based adaptation and student modeling in QuizGuide. User Modeling and User-Adapted Interaction, Vol. 25, No. 4, pp. 371–424.

      [20] Johnson L, Adams Becker S, Cummins M, Estrada V, Freeman A & Hall C (2016), NMC horizon report: 2016 higher education edition. The New Media Consortium, Austin, Texas.

      [21] Sosnovsky S & Brusilovsky P (2005), Layered evaluation of topic-based adaptation to student knowledge. User Modeling 2005, Ardissono L, Brna P & Mitrović A, Eds. Berlin, Heidelberg: Springer, pp. 47–56.

      [22] Sammut C & Webb GI (2010), Encyclopedia of Machine Learning. Boston, MA: Springer US.

      [23] Chrysafiadi K & Virvou M (2015), Fuzzy Logic for adaptive instruction in an E-learning environment for Computer Programming. IEEE Transactions on Fuzzy Systems, Vol. 23, No. 1, pp. 164–177.

      [24] Voskoglou MG & Subbotin IY (2015), A Fuzzy Model for Measuring the Student Learning Based on Bloom's Taxonomy. Egyptian Computer Science Journal, Vol. 39, No. 2, pp. 43–55.

      [25] Mehdi Khosrow-Pour DBA (2018), Encyclopedia of information science and technology (4th ed.). Hershey: IGI Global.

      [26] Leony D, Pardo A, de la Fuente Valentín L, de Castro DS & Kloos CD (2012), GLASS: A learning analytics visualization tool. LAK ‘12 Proceedings of the 2nd international conference on learning analytic and knowledge, pp. 162–163.

      [27] Schmitz HC, Scheffel M, Friedrich M, Jahn M, Niemann K & Wolpers M (2009), CAMera for PLE. Learning in the synergy of multiple disciplines. Cress U, Dimitrova V & Specht M, Eds. Berlin, Heidelberg: Springer, pp. 507–520.

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  • How to Cite

    Thing Ng, W., Siong Teh, C., & Jen Chen, C. (2018). Design and Development of Topic-based Students’ Knowledge Modelling System using Fuzzy Set Theory and Visual Analytics. International Journal of Engineering & Technology, 7(4.31), 87-94. https://doi.org/10.14419/ijet.v7i4.31.23347

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09