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Utilization of two web-based continuing education courses evaluated by Markov chain model

Utilization of two web-based continuing education courses evaluated by Markov chain model AbstractObjectives To evaluate the web structure of two web-based continuing education courses, identify problems and assess the effects of web site modifications.Design Markov chain models were built from 2008 web usage data to evaluate the courses' web structure and navigation patterns. The web site was then modified to resolve identified design issues and the improvement in user activity over the subsequent 12 months was quantitatively evaluated.Measurements Web navigation paths were collected between 2008 and 2010. The probability of navigating from one web page to another was analyzed.Results The continuing education courses' sequential structure design was clearly reflected in the resulting actual web usage models, and none of the skip transitions provided was heavily used. The web navigation patterns of the two different continuing education courses were similar. Two possible design flaws were identified and fixed in only one of the two courses. Over the following 12 months, the drop-out rate in the modified course significantly decreased from 41% to 35%, but remained unchanged in the unmodified course. The web improvement effects were further verified via a second-order Markov chain model.Conclusions The results imply that differences in web content have less impact than web structure design on how learners navigate through continuing education courses. Evaluation of user navigation can help identify web design flaws and guide modifications. This study showed that Markov chain models provide a valuable tool to evaluate web-based education courses. Both the results and techniques in this study would be very useful for public health education and research specialists. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Utilization of two web-based continuing education courses evaluated by Markov chain model

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Publisher
Oxford University Press
Copyright
© 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1136/amiajnl-2011-000287
pmid
21976027
Publisher site
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Abstract

AbstractObjectives To evaluate the web structure of two web-based continuing education courses, identify problems and assess the effects of web site modifications.Design Markov chain models were built from 2008 web usage data to evaluate the courses' web structure and navigation patterns. The web site was then modified to resolve identified design issues and the improvement in user activity over the subsequent 12 months was quantitatively evaluated.Measurements Web navigation paths were collected between 2008 and 2010. The probability of navigating from one web page to another was analyzed.Results The continuing education courses' sequential structure design was clearly reflected in the resulting actual web usage models, and none of the skip transitions provided was heavily used. The web navigation patterns of the two different continuing education courses were similar. Two possible design flaws were identified and fixed in only one of the two courses. Over the following 12 months, the drop-out rate in the modified course significantly decreased from 41% to 35%, but remained unchanged in the unmodified course. The web improvement effects were further verified via a second-order Markov chain model.Conclusions The results imply that differences in web content have less impact than web structure design on how learners navigate through continuing education courses. Evaluation of user navigation can help identify web design flaws and guide modifications. This study showed that Markov chain models provide a valuable tool to evaluate web-based education courses. Both the results and techniques in this study would be very useful for public health education and research specialists.

Journal

Journal of the American Medical Informatics Association – Oxford University Press

Published: May 5, 2012

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