Towards Inferring Sequential-Global Dimension of Learning Styles from Mouse Movement Patterns

  • Danilo Spada
  • Manuel Sánchez-Montañés
  • Pedro Paredes
  • Rosa M. Carro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5149)

Abstract

One of the main concerns of user modelling for adaptive hypermedia deals with automatic user profile acquisition. In this paper we present a new approach to predict sequential/global dimension of Felder-Silverman’s learning style model that only makes use of mouse movement patterns. The results obtained in a case study with 18 students are very promising. We found a strong correlation between maximum vertical speed and sequential/global dimension score. Moreover, it was possible to predict whether students’ learning styles are global or sequential with high accuracy (94.4%). This suggests that mouse movement patterns can be a powerful source of information about certain user features.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Danilo Spada
    • 1
  • Manuel Sánchez-Montañés
    • 1
  • Pedro Paredes
    • 1
  • Rosa M. Carro
    • 1
  1. 1.Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

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