Proficiency-adapted information browsing and filtering in hypermedia educational systems

We present a framework for proficiency-adapted information browsing and filtering in educational hypermedia systems. In hyperdocuments, information is acquired by browsing through highly interconnected sets of information nodes. In order to find specific information, users follow links to nodes they judge to be relevant. In order to help users find relevant information and new learning material that match their levels of domain knowledge, we present a framework for adapting the information nodes, and the links leading to them, to the user's proficiency in the subject matter. Such a proficiency-adapted, user-centered educational environment is intended to enhance learning. We believe that learning in educational hypertext-based applications cannot be reduced to traversing a static information space. Navigating through any space, be it a physical or an information space, normally requires that users have a prior degree of proficiency in the domain knowledge. Learning is an evolving dynamic process through which users progress from a situation of unfamiliarity to one of mastery of a knowledge corpus. Therefore, we propose a model of proficiency-adapted learning and information browsing in which the presented choices (links and the textual context of links) are selected based on the user's knowledge state. Ultimately, such an adaptive course not only guides the learning process of the student, but it gradually transforms itself into a reference guide.