A Study of a Learning Style Index to Support an Intelligent and Adaptive Learning Systems

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 17)

Abstract

An intelligent and adaptive learning system should adjust the content in order to ensure a faster and better performance in the learning process. One way is to help the learners and teachers to discover the preferences of learners. A learning style index is a method to classify the learning preferences of learners. Learning preferences can then help learners to find their most effective way to learn. It can also help teachers to adopt suitable learning materials for an efficient learning. This chapter is concerned with the study, implementation, and application of a web-based learning style index. We also describe a case study on the integration of the learning style index into an adaptive and intelligent e-learning system.

Keywords

Teaching Style Learning Preference Visual Learner Junior High School Student Reflective Learner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. ADL. Website (2007), http://www.adlnet.gov (acessed February 21, 2011)
  2. ASF. Website (2008), http://www.apache.org (acessed December 01, 2010)
  3. Felder, R., Silverman, L.: Learning and teaching styles in engineering education. Engineering Education 78(7), 674–681 (1988)Google Scholar
  4. Hamada, M.: An integrated virtual environment for active and collaborative e-learning in theory of computation. IEEE Transactions on Learning Technologies 1(2), 1–14 (2008)CrossRefGoogle Scholar
  5. Herrmann, N.: The Creative Brain. Brain Book, Lake Lure (1990)Google Scholar
  6. Keller, J.: Development and use of the ARCS model of motivational design. Journal of Instructional Development 10(3), 2–10 (1987)CrossRefGoogle Scholar
  7. Kolb, D.: Experiential learning: experience as the source of learning and development. Prentice-Hall, Englewood Cliffs (1984)Google Scholar
  8. Kort, B., Reilly, R., Picard, R.W.: An affective model of interplay between emotions and learning: reengineering educational pedagogy- building a learning companion. In: Proceedings of ICALT, pp. 43–46. IEEE Press, New York (2001)Google Scholar
  9. Kumiko, F., Mari, M.: Cloninger’s temperament dimensions, emotional experiences and emotional regulation. Yamagata Univ. Educ. Sci. 14(4), 387–397 (2009) (in Japanese)Google Scholar
  10. Magnisalis, I., Demetriadis, S., Karakostas, A.: Adaptive and intelligent systems for collaborative learning support: a review of the field. IEEE Transactions on Learning Technologies 4(1), 5–20 (2011)CrossRefGoogle Scholar
  11. Silvia, R.V., Sabine, G., Kinshuk, T.L.: Analysis of Felder-Silverman index of learning styles by a data-driven statistical approach. In: Proceedings of ISM, pp. 959–964. IEEE Press, New York (2006)Google Scholar
  12. Silvia, R.V., Sabine, G., Kinshuk, T.L.: Investigating relationships within the index of learning styles; a data driven approach. Journal of Interactive Technology and Smart Education 4(2), 7–18 (2007)Google Scholar
  13. Soloman, B., Felder, R.: Index of learning style questionnaire (2009), http://www.engr.ncsu.edu/learningstyle/ilsweb.html (acessed January 20, 2010)
  14. Sun Microsystems. Java2D (2006), www.sun.com (acessed March 11, 2010)
  15. Thomas, A.L., Sang, H.L., Wise, J., Richard, F.: A Psychometric study of the index of learning styles. Journal of Engineering Education 96(4), 309–319 (2007)Google Scholar
  16. Tomcat. Website (2010), http://tomcat.apche.org (accessed December 01, 2010)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Graduate SchoolThe University of AizuAizuwakamatsuJapan
  2. 2.Direction of Primary Education of Eastern ThessalonikiThessalonikiJapan

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