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


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.


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.



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


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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

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