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A learning process is driven by “what to learn”, i.e., the scope of learning, and “how to learn”, i.e., how a student approaches such learning scope. Adaptive e-learning addresses these two questions by offering students with tailored learning materials. Existing work on adaptive e-learning tackles this problem by applying student profiles on well organized courseware. More specifically, a student profile captures the learning preferences, background knowledge/experiences and learning progress of the student. It forms the basis for filtering a pool of course materials to pick out relevant ones. For instance, InterBook (Brusilovsky et al., 1998) organizes course materials in a hierarchical structure along with indices according to the topics and level of difficulties. (Middleton et al., 1998) improves the discovery of relevant course materials by knowledge classification based on ontology and collaborative choices made by a group of users. The ontology (Studer et al., 1998) formulates the grouping and the relation among concepts. It is commonly applied to organize course materials and to form the metric for determining the user required materials. Another example can be found in (Dolog et al., 2004). The utilization of collaborative information (Balabanović et al., 1997) can enhance the accuracy of the retrieved course materials, as it complements the incompleteness or impreciseness of individual user profiles. (Freyne et al., 2007) has exploited user browsing and searching patterns to give more precise modeling on collaborative information. All of the above methods focus on addressing the “what to learn” problem.