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Abstract-The amazing usage of internet as a communication channel has changed the very structure of companies activities, being education a clear example of this situation.Though data mining has been successfully used to improve the 1-to-1 relationship in e-commerce sites, when applied to elearning environments the results are often limited to provide the educator with access summary information or primitive patterns. In any case, information being obtained is not being used proactively. For enviroments to profit from the information obtained it has to be integrated in a global infrastructure. This paper presents the design of a data e-learning web-house as supporting structure for future e-learning personalized systems.

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The fiercely competitive web-based electronic commerce (e-commerce) environment has made necessary the application of intelligent methods to gather and analyze information collected from consumer web sessions. Knowledge about user behavior and session goals can be discovered from the information gathered about user activities, as tracked by web clicks. Most current approaches to customer behavior analysis study the user session by examining each web page access. However, the abstraction of subsessions provides a more granular view of user activity. Here, we propose a method of increasing the granularity of the user session analysis by isolating useful subsessions within sessions. Each subsession represents a high-level user activity such as performing a purchase or searching for a particular type of information. Given a set of previously identified subsessions, we can determine at which point the user begins a preidentified subsession by tracking user clicks. With this information we can (1) optimize the user experience by precaching pages or (2) provide an adaptive user experience by presenting pages according to our estimation of the user's ultimate goal. To identify subsessions, we present an algorithm to compute frequent click paths from which subsessions then can be isolated. The algorithm functions by scanning all user sessions and extracting all frequent subpaths by using a distance function to determining subpath similarity. Each frequent subpath represents a subsession. An analysis of the pages represented by the subsession provides additional information about semantically related activities commonly performed by users.

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