Model E-learning MDP for Learning Style Detection Using Prior Knowledge

  • Abstract
  • Keywords
  • References
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  • Abstract

    The Learning Style Detection model in e-learning systems is experiencing rapid development. This development is characterized by the existence of two learning style detection approaches namely automatic and conventional. The development of detection of automatic and conventional learning styles that exist today does not pay attention to the relationship of learning styles with prior knowledge. This is important to note because the style of learning is not static and tends to be dynamic depending on the topic of learning. This study builds the VARK MDP learning style detection model. It explores the relationship between learning styles with prior knowledge as evidenced by experiments on 32 learners. There are three steps taken: Measurement Prior Knowledge, Determine Prior Knowledge, Preference Learning Style. To evaluate this model we built detection scenarios with prior knowledge and compared with the results of interviews based on VARK learning style questionnaire. This study succeeded in building a model of measurement of prior knowledge that is more accurate than the previous model. Detection results also show that every learner does not only have one learning style and changes according to the topic.



  • Keywords

    Dectecting Learning Style, Prior Knowledge, VARK.

  • References

      [1] [A. Y. Kolb and D. a Kolb, “The Kolb Learning Style Inventory — Version 3 . 1 2005 Technical Specifi cations,” LSI Tech. Man., pp. 1–72, 2005.

      [2] N. D. Fleming, “I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom,” Res. Dev. High. Educ. Proc. Annu. Conf. High. Educ. Res. Dev. Soc. Australasi, pp. 308–313, 1995.

      [3] S. Graf, S. Viola, and Kinshuk, “Automatic student modelling for detecting learning style preferences in learning management systems,” IADIS Int. Conf. Cogn. Explor. Learn. Digit. Age, no. 1988, pp. 172–179, 2007.

      [4] A. Amran, A. Desiani, and M. Hasibuan, “DETECTION LEARNING STYLE VARK FOR OUT OF SCHOOL CHILDREN (OSC).”

      [5] M. S. Hasibuan, “Detecting Learning Style Using Hybrid Model,” in IEEE Conference On E-learning, E-Management, and E-Service, 2016.

      [6] W. F. F. Yahya and N. M. M. Noor, “Decision Support System for Learning Disabilities Children in Detecting Visual-Auditory-Kinesthetic Learning Style,” 7th Int. Conf. Inf. Technol., vol. 2015, pp. 667–671, 2015.

      [7] S. Graf, “Advanced Adaptivity in Learning Management Systems by Considering Learning Styles *,” IEEE Int. Conf. web Intell. agent Technol., 2009.

      [8] T. Hailikari, Assessing University Students ’. Helsinki University Print, Finland, 2009.

      [9] A. Latham, K. Crockett, and D. Mclean, “Profiling Student Learning Styles with Multilayer Perceptron Neural Networks,” 2013 IEEE Int. Conf. Syst. Man, Cybern., pp. 2510–2515, 2013.

      [10] S. Moazeni and H. Pourmohammadi, “Smart teaching quantitative topics through the VARK learning styles model,” ISEC 2013 - 3rd IEEE Integr. STEM Educ. Conf., 2013.

      [11] Kolb, Experiential Learning: Experience as the Source of Learning and Development [Paperback], no. December. 1983.

      [12] J. Rosewell, Learning styles, no. 4659. 2005.

      [13] T. Hamtini, “A Proposed Dynamic Technique for Detecting Learning Style Using Literature Based Approach,” IEEE jordan Conf. an Appl. Electr. Eng. Comput. Technol., 2015.

      [14] M. P. P. Liyanage, K. S. L. Gunawardena, and M. Hirakawa, “Using Learning Styles to Enhance Learning Management Systems,” Chapter I, vol. 07, no. 02, pp. 1–10, 2014.

      [15] H. L. Lujan and S. E. DiCarlo, “First-year medical students prefer multiple learning styles.,” Adv. Physiol. Educ., vol. 30, no. 1, pp. 13–16, 2006.

      [16] D. S. S. Sahid, L. E. Nugroho, and P. I. Santosa, “Integrated stochastic and literate based driven approaches in learning style identification for personalized E-learning purpose,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 5, pp. 1708–1715, 2017.

      [17] V. Marcy, “Adult learning styles: How the VARK Learning Styles Inventory can be used to improve student learning,” Perspect. Physician Assist. Educ., vol. 12, no. 2, pp. 117–120, 2001.

      [18] H. Moayyeri, “The Impact of Undergraduate Students’ Learning Preferences (VARK Model) on Their Language Achievement,” J. Lang. Teach. Res., vol. 6, no. 1, pp. 132–139, 2015.

      [19] W. A. Drago and R. J. Wagner, “Vark preferred learning styles and online education,” Manag. Res. News, vol. 27, no. 7, pp. 1–13, 2004.

      [20] S. Graf, Kinshuk, and T. C. Liu, “Identifying learning styles in learning management systems by using indications from students’ behaviour,” Proc. - 8th IEEE Int. Conf. Adv. Learn. Technol. ICALT 2008, pp. 482–486, 2008.

      [21] F. S. Pribadi, E. Permanasari, and T. Ninomiya, “Short Answer Scoring Using W-Bleu For Reguler Assessment In Vocational High School,” Ijil, 2018.

      [22] K. Papineni, S. Roukos, T. Ward, and W. Zhu, “BLEU: a method for automatic evaluation of machine translation,” … 40Th Annu. Meet. …, no. July, pp. 311–318, 2002.

      [23] J. M. Lampinen and J. D. Arnal, “A Revision of Bloom’s Taxonomy: An Overview,” Am. J. Psychol., vol. 122, no. 1, pp. 39–52, 2009.

      [24] T. Hailikari, Assessing University Students, vol., no. 2009.




Article ID: 24416
DOI: 10.14419/ijet.v7i4.40.24416

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