A model-driven data marshalling approach for capturing learning activities in heterogeneous environments

The approach presented in this paper aims for a technological contribution to online learning research and especially learning analytics discussing the model-driven data marshalling approach developed in the context of a large-scale European research project. The approach enables a transparent, end-user driven integration of learning applications and services in a distributed heterogeneous environment. The approach builds on service-oriented marshalling of data utilizing meta-modelling concepts and technologies as a realisation environment on design level, combined with interpretative workflow technologies on execution level. The application case presented in this paper relates to realizing enhanced assessment methods for online learning on a learning platform independent level.