Analysis of Learners' Navigational Behaviour and Their Learning Styles in an Online Course
ARTICLE
S Graf, T-C Liu, Kinshuk
Journal of Computer Assisted Learning Volume 26, Number 2, ISSN 1365-2729 Publisher: Wiley
Abstract
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.
Citation
Graf, S., Liu, T.C. & Kinshuk. (2010). Analysis of Learners' Navigational Behaviour and Their Learning Styles in an Online Course. Journal of Computer Assisted Learning, 26(2), 116-131. Wiley. Retrieved August 31, 2024 from https://www.learntechlib.org/p/108289/.
ERIC is sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education.
Copyright for this record is held by the content creator. For more details see ERIC's copyright policy.
Keywords
Cited By
View References & Citations Map-
Tracking e-learning through published papers: A systematic review
Helena Rodrigues, Filomena Almeida, Vanessa Figueiredo & Sara L. Lopes, Instituto Universitário de Lisboa (ISCTE-IUL), Portugal
Computers & Education Vol. 136, No. 1 (July 2019) pp. 87–98
-
Exploring Data Visualization as an Emerging Analytic Technique
Min Liu, University of Texas at Austin, United States; Jina Kang, Pan Zilong, Wenting Zou & Hyeyeon Lee, Univ. of Texas at Austin, United States
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2017 (Oct 17, 2017) pp. 1681–1690
-
The Effects of Self-Regulated Learning Strategies in Hypermedia Based Educational Environments
Heather Keahey & Robert Wright, University of North Texas, United States
Society for Information Technology & Teacher Education International Conference 2016 (Mar 21, 2016) pp. 2120–2124
-
Online Instructors as Thinking Advisors: A Model for Online Learner Adaptation
Christopher Benedetti
Journal of College Teaching & Learning Vol. 12, No. 3 (2015) pp. 171–176
-
Linking Learning Styles and Learning on Mobile Facebook
Yu-ching Chen
The International Review of Research in Open and Distributed Learning Vol. 16, No. 2 (Apr 15, 2015) pp. 94–114
-
An Examination of Students’ Learning Styles and Motivation in an Online Learning Environment: A Critical Review
Seung-Hae (Diana) Bang, The University of British Columbia, Canada
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2011 (Oct 18, 2011) pp. 1817–1820
-
The Relationship between Learning Styles and Student Learning in Online Courses
Susan Featro, Wilkes University, United States
EdMedia + Innovate Learning 2011 (Jun 27, 2011) pp. 3431–3438
-
The Relationship Between Learning Styles and Student Learning in Online Courses
Susan Featro, Wilkes University, United States
Society for Information Technology & Teacher Education International Conference 2011 (Mar 07, 2011) pp. 266–273
These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact info@learntechlib.org.