TY - JOUR
T1 - Analysis of learners' navigational behaviour and their learning styles in an online course
AU - Graf, S.
AU - Liu, T. C.
AU - Kinshuk,
PY - 2010/4
Y1 - 2010/4
N2 - 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.
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.
KW - Learning management systems
KW - Learning styles
KW - Navigational behaviour
KW - Sequential analysis
UR - http://www.scopus.com/inward/record.url?scp=77954380774&partnerID=8YFLogxK
U2 - 10.1111/j.1365-2729.2009.00336.x
DO - 10.1111/j.1365-2729.2009.00336.x
M3 - Journal Article
AN - SCOPUS:77954380774
SN - 0266-4909
VL - 26
SP - 116
EP - 131
JO - Journal of Computer Assisted Learning
JF - Journal of Computer Assisted Learning
IS - 2
ER -