Knowledge Representation and Reasoning in Personalized Web-Based e-Learning Applications

Adaptation that is so natural for teaching by humans is a challenging issue for electronic learning tools. Adaptation in classic teaching is based on observations made about students during teaching. Similar idea was employed in user-adapted (personalized) eLearning applications. Knowledge about a user inferred from user interactions with the eLeanrning systems is used to adapt o®ered learning resources and guide a learner through them. This keynote gives an overview about knowledge and rules taken into account in current adaptive eLearning prototypes when adapting learning instructions. Adaptation is usually based on knowledge about learning esources and users. Rules are used for heuristics to match the learning resources with learners and infer adaptation decisions.