| Abstrakt: |
The rapid and huge growth of the web has emphasized the need to monitor the behavior of web users and to identify their interest, knowledge, preferences, goals, etc. This paper introduces a methodology for classifying users and pages of an educational online hypermedia using a hybrid clustering technique based on Self Organizing Map (SOM) neural networks. This paper also introduces an analytical cluster validation and interpretation approach to verify and explain the generated clusters of users and pages. The implemented cluster validation process utilizes a silhouette-based quantitative measure, while a combined data visualization and statistical cluster interpretation technique is proposed. Several experiments have been carried out using real data collected through special lab sessions of real students navigating an online tutorial. Experimental results indicated that the proposed methodology was able to prototype users and to recognize the association between pages based on their usage. Moreover, the topic of interest and the users interested in these topics were also identified. [ABSTRACT FROM AUTHOR] |
