Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths

Abstract

In the area of e-learning, there are new challenges to be overcome. One of them is to allow students self-regulation of their learning. Within this self-regulation process, students and teachers use technological tools to carry out their tasks. In general, these tools do not consider pedagogical aspects. Emerging approaches propose the use of electronic portfolios and semantic technologies to provide pedagogical characteristics that make tools more user-friendly for both teachers and students. This paper presents a conceptual model of an intelligent system to support self-regulated learning. This model is based on semantic technologies and uses the concepts of learning paths and student e-portfolios. For learning path generation, the system considers the different learning levels that a student should achieve and metrics that measure student progress. In this way, it proposes a learning path with the intention that the student achieves as highest learning level as possible.