An Analysis of Open Learner Models for Supporting Learning Analytics

Abstract

Teaching and learning are increasingly being offered in distributed, online digital environments, often openly and at large-scale, traversing spatial and temporal boundaries. Within such environments, Learning Analytics technologies aim to provide the means for tracking and making sense of the multitude of educational data that is being generated, in order to inform educational and pedagogical decision making of different actors, such as learners, teachers, school leaders and parents. However, at the heart of Learning Analytics technologies in such distributed and open learning environments lies the Open Learner Model (OLM), that informs the data collection, processing and sense-making capabilities of the analytics technology. In this context the contribution of this chapter is to present a generic educational data-driven layered Open Learner Modelling framework, which can be used as a blueprint for the analysis (and design) of OLM instances. Furthermore, capitalizing on this framework, the chapter also performs a critical analysis of existing research in OLM works, in order to draw conclusions on the current status of this emerging field.