Analysis of learners' navigational behaviour and their learning styles in an online course.

Providing adaptive features and personalized support by considering students' learning styles in computer-assisted learning systems has high potential in making learning easier for students in terms of reducing their efforts or increasing their performance. In this study, the navigational behaviour...

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Publicado en:Journal of Computer Assisted Learning Vol. 26; no. 2; pp. 116 - 132
Autores principales: Graf S, Liu T, Kinshuk
Formato: pictorial research tables/charts Journal Article
Publicado: Wiley-Blackwell Apr2010
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2010
      vid: 26
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1111/j.1365-2729.2009.00336.x
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        atl: Analysis of learners' navigational behaviour and their learning styles in an online course.
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          Graf S
          Liu T
          Kinshuk
        affil: Athabasca University, School of Computing and Information Systems, Alberta, Canada
      sug:
        subj:
          Behavior
          Education, Non-Traditional
          Internet Utilization
          Learning Methods
          Austria
          Education, Baccalaureate
          Funding Source
          Human
          Questionnaires
      ab: Providing adaptive features and personalized support by considering students' learning styles in computer-assisted learning systems has high potential in making learning easier for students in terms of reducing their efforts or increasing their performance. In this study, the navigational behaviour of students in an online course within a learning management system was investigated, looking at how students with different learning styles prefer to use and learn in such a course. As a result, several differences in the students' navigation patterns were identified. These findings have several implications for improving adaptivity. First, they showed that students with different learning styles use different strategies to learn and navigate through the course, which can be seen as another argument for providing adaptivity. Second, the findings provided information for extending the adaptive functionality in typical learning management systems. Third, the information about differences in navigational behaviour can contribute towards automatic detection of learning styles, helping in making student modeling approaches more accurate.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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