Lifelong Learner Modeling in Academic Networks
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 was to combine Web, text and interest mining techniques to the distributed publication information from various academic networks to build an academic learner model.
Learning analytics (LA) deals with the development of methods that harness educational data sets to support the learning process. To achieve particular learner-centered LA objectives such as intelligent feedback, adaptation, personalization, or recommendation, lifelong learner modeling is a crucial task. Lifelong learner modeling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. In this work, we focus on lifelong learner modeling in academic networks. We present theoretical, design, implementation, and evaluation details of PALM, a service for personal academic learner modeling. The primary aim of PALM is to harness the distributed publication information to build an academic learner model.
PALM is currently deployed here.
Created by. Last Modification: Friday, 30. November 2012 11:49:23 by .