Advertisement

Facilitating Learning Through Dynamic Student Modelling of Learning Styles

An Architecture and Its Application for Providing Adaptivity
  • Sabine GrafEmail author
  • Kinshuk
  • Qingsheng Zhang
  • Paul Maguire
  • Victoria Shtern
Chapter

Abstract

Technology enhanced learning environments usually track a variety of data about students’ behaviour while students are learning. These data can be used to infer valuable information about how students learn as well as about their characteristics. This chapter focuses on the consideration of learning styles in technology enhanced learning. Considering students’ learning styles in technology enhanced learning can have many benefits for students such as providing them with personalized recommendations and advice based on their learning styles. In this chapter, we introduce an architecture that aims at dynamically identifying students’ learning styles from their behaviour in a learning system by frequently checking students’ behaviour and updating their learning styles based on their recent behaviour. Such a dynamic student modelling approach enables systems to incrementally learn students’ learning styles, identify and consider exceptional behaviour of students, and respond to changes in students’ learning styles by updating the student model respectively. The proposed architecture has been developed with only few dependencies to the learning system, making it possible to easily adjust and use the architecture for different learning systems. In this chapter, the integration of the proposed architecture into a particular learning system is demonstrated. Furthermore, an adaptivity module has been developed to show the benefits of the proposed architecture. This adaptivity module accesses the information about students’ learning styles to provide students with adaptive feedback about their learning styles as well as about how to improve their learning processes considering their learning styles and their courses.

Keywords

Dynamic Student Modeling Felder-Silverman Learning Style Model (FSLSM) Data Extraction Module Dynamic Modal Analysis 
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.

Notes

Acknowledgements

The authors acknowledge the support of NSERC, iCORE, Xerox, and the research related gift funding by Mr. A. Markin.

References

  1. Bajraktarevic, N., Hall, W., & Fullick, P. (2003). Incorporating learning styles in hypermedia environment: Empirical evaluation. In P. de Bra, H. C. Davis, J. Kay & M. Schraefel (Eds.), Proceedings of the Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 41–52). Nottingham, UK: Eindhoven University.Google Scholar
  2. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6 (2–3), 87–129.CrossRefGoogle Scholar
  3. Carver, C.A., Howard, R.A., & Lane, W.D. (1999). Addressing different learning styles through course hypermedia. IEEE Transactions on Education, 42 (1), 33–38.CrossRefGoogle Scholar
  4. Cha, H.J., Kim, Y.S., Park, S.H., Yoon, T.B., Jung, Y.M., & Lee, J.-H. (2006). Learning style diagnosis based on user interface behavior for the customization of learning interfaces in an intelligent tutoring system. In M. Ikeda, K. D. Ashley & T.-W. Chan (Eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Lecture Notes in Computer Science (pp. 513–524). Berlin, Heidelberg: Springer, Vol. 4053.CrossRefGoogle Scholar
  5. Felder, R.M., & Silverman, L.K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78 (7), 674–681.Google Scholar
  6. Felder, R.M., & Soloman, B.A. (1997). Index of Learning Styles questionnaire. Retrieved 14 March, 2011, from http://www.engr.ncsu.edu/learningstyles/ilsweb.html.
  7. García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49 (3), 794–808.CrossRefGoogle Scholar
  8. Graf, S., & Kinshuk (2007). Providing adaptive courses in learning management systems with respect to learning styles. In G. Richards (Ed.), Proceedings of the world conference on e-learning in corporate, government, healthcare, and higher education (e-Learn 2007) (pp. 2576–2583). Chesapeake, VA: AACE Press.Google Scholar
  9. Graf, S., & Kinshuk (2009). An approach for dynamic student modelling of learning styles. Proceedings of the International Conference on Exploratory Learning in Digital Age (CELDA 2009) (pp. 462–465). Rome, Italy: IADIS press.Google Scholar
  10. Graf, S., Kinshuk, & Liu, T.-C. (2009). Supporting teachers in identifying students’ learning styles in learning management systems: An automatic student modelling approach. Educational Technology & Society, 12 (4), 3–14.Google Scholar
  11. Khan, F.A., Graf, S., Weippl, E.R., & Tjoa, A.M. (2010). Identifying and Incorporating Affective States and Learning Styles in Web-based Learning Management Systems. International Journal of Interaction Design & Architectures, 9–10, 85–103.Google Scholar
  12. Kinshuk, & Lin, T. (2004). Cognitive profiling towards formal adaptive technologies in web-based learning communities. International Journal of WWW-based Communities, 1 (1), 103–108.CrossRefGoogle Scholar
  13. Kuljis, J., & Liu, F. (2005). A comparison of learning style theories on the suitability for elearning. In M. H. Hamza (Ed.), Proceedings of the IASTED Conference on Web Technologies, Applications, and Services (pp. 191–197). Calgary, Alberta: ACTA Press.Google Scholar
  14. Lin, T. (2007). Cognitive Trait Model for adaptive learning environments. PhD thesis, Massey University, Palmerston North, New Zealand.Google Scholar
  15. Özpolat, E., & Akar, G.B. (2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53 (2), 355–367.CrossRefGoogle Scholar
  16. Popescu, E. (2010). Adaptation provisioning with respect to learning styles in a web-based educational system: An experimental study. Journal of Computer Assisted Learning, 26 (4), 243–257.CrossRefGoogle Scholar
  17. Shang, Y., Shi, H., & Chen, S.-S. (2001). An intelligent distributed environment for active learning. ACM Journal of Educational Resources in Computing, 1 (2), 1–17.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Sabine Graf
    • 1
    Email author
  • Kinshuk
    • 1
  • Qingsheng Zhang
    • 1
  • Paul Maguire
    • 1
  • Victoria Shtern
    • 1
  1. 1.School of Computing and Information SystemsAthabasca UniversityAthabascaCanada

Personalised recommendations