P Pu ur rp po os se e: : Prediction of difficulty in orotracheal intubation (DI) in patients undergoing laryngeal microsurgery should help reduce the morbidity-mortality associated with this clinical situation. To establish a simple score to predict this difficulty, we studied 11 variables and their association with DI in these patients.

In this paper we describe the navigational behavior of the students of a e-learning virtual environment, in order to determine whether such navigational patterns are related to the academic performance achieved by the students or not, and which behaviors can be identified as more successful . IntroductionWeb mining is becoming a useful and common tool for institutions, as more and more data is collected from the users browsing the increasing number of web pages with interesting content. The validity of web mining as a tool for extracting useful information in any web-based organization system is described in several papers, see [13,7] for example. There are several fields where web mining can be used for understanding user navigational behavior. This expertise about users behavior can be reintegrated in the webbased system (offering user personalized services, for example) in order to improve user experience and satisfaction, and hopefully, to strength the customer relationship model.On the other hand, e-learning is one of the most promising and growing issues in the information society nowadays. The growth of the Internet is bringing online learning to people in corporations, institutes of higher education, the government and other sectors. The growing need of continuous education and the inclusion of new multimedia technologies become crucial factors for this expansion. The appear of Learning Management Systems (LMS) has been a remarkable event for the success of e-learning environments, because there is no longer the need to design specific software for both content delivery and user management.Several interesting questions arise from the use of web mining techniques in e-learning virtual environments. The possibility of tracking user behavior in such environments creates new possibilities for both web-based system architects and designers, but also for the pedagogical and instructional designers, which create and organize the learning contents. One of the most interesting possibilities is the personalization of the e-learning process. Personalization, which is a term widely used in other environments [11] such as e-commerce, is one of the most well-known and desirable properties of any web-based system, as it pursues the improvement of user experience and satisfaction. Personalization arises from the knowledge extracted from the navigational behavior of the e-learning virtual environment users, mostly students in this particular scenario. In fact, such scenario is a "closed" system in the sense that every action performed by the users are related to the learning process, and with a set of previously established goals. Therefore, interesting hypothesis about users behavior, navigational patterns and other issues related to the learning process can be formulated and validated by means of web mining tools. This paper is structured as follows: Section 2 describes the internal structure of e-learning virtual environments and the navigational levels. The experiments which support the user navigational beh...

Personalization is an important issue in e-learning as it might help to improve both student performance and experience of use. In this chapter we describe a framework for studying the navigational behavior of the users of an e-learning environment integrated in a virtual campus. The students navigate through the web based virtual campus interacting with learning resources which are structured following the SCORM e-learning standard. These learning resources are structured following the concept of itinerary which it is basically a temporal scheduling involving several activities and the use of several learning resources. Itineraries may be structured depending on several personalization issues, ranging from student preferences to instructional designer and teacher teams expertise, including also knowledge extracted from the usage in previous semesters. Our main goal is to analyze such user navigational behavior for extracting information that can be used to validate several aspects related to virtual campus design and usability but also to determine the optimal scheduling for each course depending on user profile. We intend to extend the sequencing capabilities of standard learning management systems to include the concept of recommended itinerary, by combining teachers expertise with learned experience acquired by virtual campus usage analysis.

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