Learner Modeling in Academic Networks

Learning analytics (LA) deals with the development of methods that harness educational data sets to support the learning process. To achieve particular learner entered LA objectives such as intelligent feedback, adaptation, personalization, or recommendation, learner modeling is a crucial task. 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 paper, we focus on 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.