By Shi-Kuo Chang (auth.), Joseph Fong, Chu Ting Cheung, Hong Va Leong, Qing Li (eds.)

This booklet constitutes the refereed court cases of the 1st foreign convention on Web-Based studying, ICWL 2002, held in Hong Kong, China in August 2002.
The 34 revised complete papers offered including an invited keynote paper have been conscientiously reviewed and chosen from seventy five submissions. The papers are prepared in topical sections on method modeling and architectures, distance studying platforms engineering, collaborative platforms, reports in distance studying, databases and information mining, and multimedia.

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In order to validate the system, we have to gather more data from the students. The data should not only reflect what questions the students asked, as in the search engine query logs, but also how they rank the returned results. Given these question-answer log files, we can apply the above learning algorithm and keep the question to answer mapping always current [12, 15]. 2 The “Everyone Is Different” Personalized Service In a traditional education system, the course content is static and the teacher’s assignments given to different students are the same.

Ties are broken arbitrarily. Ui rules. The first rule representation we consider is called the subset rules. These rules are the same as the traditional association rules which simply ignore the order and adjacency between accesses. Thus, when the association rule mining methods, such as the Aprioi method [4,5,6], are applied to the log table, we obtain the subset rules. As an example, table 1 shows the subset rules that can be extracted from a transaction. Table 1. Subset rules W1 A, B, C W2 D Subset Rules {A, B, C} !

The goal match mechanism will compare the user's goals with the description items (metadata content) of the learning objects stored and maintained in the repository. Once a match is found, the matched learning object will be presented to the user. If there is more than one goal to match, then the mechanism will make an integration of all the goal matches with tuning support from the users’ preferences and profiles. The intelligent interface accomplishes three tasks. Firstly, it allows the information providers to easily put their learning objects to a right place in the repository, along with a set of suitable metadata descriptions of the objects.

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