Abstract. Students in a learning experience can be seen as a commu-nity working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out nu-merous actions that affect their learning. But those actions happening outside a class or the Learning Management System cannot be easily observed. This paper presents a technique to widen the observability of these actions. The set of documents browsed by the students in a course was recorded during a period of eight weeks. These documents are then processed and the set with highest similarity with the course notes are selected and recommended back to all the students. The main problem is that this user community visits thousands of documents and only a small percent of them are suitable for recommendation. Using a combination of lexican analysis and information retrieval techniques, a fully automatic procedure to analyze these documents, classify them and select the most relevant ones is presented. The approach has been validated with an em-pirical study in an undergraduate engineering course with more than one hundred students. The recommended resources were rated as "relevant to the course" by the seven instructors with teaching duties in the course.
Recommenders are central in current applications to help the user find useful information spread in large amounts of data. Most Recommenders are more effective when huge amounts of user data are available in order to calculate user similarities. In general, educational applications are not popular enough in order to generate large amount of data. In this context, rule-based Recommenders are a better solution. Meta-rules can generalize a rule-set, providing bases for adaptation. The authors present a meta-rule based Recommender as an effective solution to provide a personalized recommendation to the learner, which is a new approach in rule-based Recommender Systems.
Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.
OPScript is a statically typed object oriented language, created to be executed as client code in Web pages. Among other features, the language supports class persistent additions, verbal communication, first class types and the definition of object models in XML. Class persistent additions are an elegant solution to the problem of the stateless condition of the HTTP protocol and also it reduces interaction with the server. Verbal communication makes transparent to the programmer the communication between objects, being them in the client machine or in the server. This feature makes code reutilization easier and makes the application independent of a specific technology.
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