GLUE!-PS: A multi-language architecture and data model to deploy TEL designs to multiple learning environments

Abstract

The complexity of orchestrating TEL scenarios prompts for a careful learning design by practitioners. Currently, teachers can use variety of tools and languages to express their designs, but they are unlikely to be supported in deploying such designs in the learning environment of their choice. This paper describes a multi-tier architecture and data model to support the deployment of learning designs, expressed in multiple languages, to different learning platforms. The proposal strives to be sustainable in authentic scenarios by minimising both software development costs and changes to current installations. The architecture and data model are theoretically validated through the transformation of a well-known learning scenario from several design languages to different learning platforms, preserving the design's essential characteristics. © 2011 Springer-Verlag Berlin Heidelberg.