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Hypermedia Browsing Pattern Analysis

, , National Chiao Tung University, Taiwan

IJET Volume 1, Number 2, ISSN 1077-9124 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA


Hypermedia course-on-demand has become a focus of distance education through computer networks. In this paper we propose a quantitative approach for hypermedia browsing pattern analysis. Although the importance of navigation behavior analysis has long been addressed, only a few researchers have discussed how to classify patterns based on objective, quantitative data recorded during a learning session.We show in this paper how to define measures based on graph theory and how to associate navigation information with other student learning activities in a hypermedia environment.

We first introduce a distant cooperative learning group project in Taiwan and the role of this study in it. We then define measurement indices for a hypermedia tutoring system. A quantitative approach to determining the similarity between navigation patterns is introduced based on the Longest Common Subsequence of two browsing paths. This partial resemblance, which, together with other metric measures, provides a sound basis for similarity analysis. A model for association, called a neuro-fuzzy classifier, is then described to complete this quantitative model. Preliminary experiment results are discussed. We believe that the proposed measures and models suggest an effective approach to student modeling in hypermedia-based distance education.


Sun, C.T. & Ching, Y.T. (1995). Hypermedia Browsing Pattern Analysis. International Journal of Educational Telecommunications, 1(2), 293-308. Charlottesville, VA: Association for the Advancement of Computing in Education (AACE). Retrieved September 19, 2019 from .



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