Learner Profiling in Open Learning Environments
In the context of the upcoming Professional Responsive Mobile Personal Learning Environment (PRiME) project, we investigate recommendation techniques in Personal Learning Environments (PLE). Recommender systems have become an important research area in Technology Enhanced Learning (TEL) over the past couple of years. They provide an effective mechanism to deal with the information overload problem. Generally, recommender systems aggregate data about user's behavior and preferences in order to draw conclusions for recommendation of items he or she most likely might be interested in. Crucial in recommender systems is the creation of an appropriate user/learner profile.
The aim of this thesis is to leverage Web mining techniques in order to build a learner profile based on information collected from the learner's interaction with various learning tools and services within an open learning environment. The Web development part of the thesis should be based on Google Web Toolkit (GWT).
Created by Daniel Herding.. Last Modification: Wednesday, 23. November 2011 16:55:29 by