Web-based collaboration calls for professional skills and competences to the benefit of the quality of the collaboration and its output. Within this framework, educational virtual environments may provide a means for training upon these skills and in particular the collaborative ones. On the basis of the existing technological means such training may be enhanced even more. Designing considerations towards this direction include the close follow-up of the collaborative activity and provision of support grounded upon a pedagogical background. To this vein, a fuzzy logic-based expert system, namely Collaboration/Reflection-Fuzzy Inference System (C/R-FIS,) is presented in this paper. By means of interconnected FISs, the C/R-FIS expert system automatically evaluates the collaborative activity of two peers, during their asynchronous, written, web-based collaboration. This information is used for the provision of adaptive support to peers during their collaboration, towards equilibrium of their collaborative activity. In particular, this enhanced formative feedback aims at diminishing the possible dissonance between the individual collaborative skills by challenging self-adjustment procedures. The proposed model extents the evaluation system of a web-based collaborative tool namely Lin2k, which has served as a test-bed for the C/R-FIS experimental use. Results from its experimental use have proved the potentiality of the proposed model to

For example, adaptive navigation support aims to share learners' cognitive load and prevent learners from disorientation. To handle cognitive issues, appropriate domain and user models are necessary. Dependency relations of domain concepts, users' proficiencies on topics and users' behavioral patterns are fundamental information to be modeled in general. On the other hand, a typical LO paradigm does not address these issues much . The presence of SS gradually changes the scenario. But SS still cannot perform adaptivity related to subtle cognitive effects due to the lack of corresponding models. Second, model-based approach could benefit by various intelligent technologies. Some could be applied to LO paradigm seamlessly. A promising instance is to adopt course sequencing techniques to generate adaptive presentation in a systematic manner [4]. We will discuss this approach in the next section. Besides, it is also possible to apply machine learning techniques, e.g. theory refineme