A Semantic Approach of an Adaptive and Personalized Web-Based Learning Content-The Case of AdaptiveWeb

Adapting to user context, individual features and behaviour patterns is a topic of great attention nowadays in the field of Web-based and mobile learning. A challenge is to design personalized interfaces and software enabling easy access to the learning content while being sufficiently flexible to handle changes in a user's context, perception and available resources. This paper presents a Web-based adaptation and personalization system, AdaptiveWeb, that uses cognitive aspects as its core filtering element. The proposed system focuses upon the creation of a comprehensive user profiling that combines parameters that analyze the most intrinsic users' characteristics like visual, cognitive, and emotional processing parameters as well as the "traditional" user profiling characteristics and together tend to give an optimized adapted and personalized result to the user. The use of semantics enables the openness of the system as adequately described in the paper.