Non-representational authoring of learning designs: from idioms to model-driven development

Abstract

Diverse authoring approaches and tools have been designed to assist the creation of units of learning compliant to current learning technology speci¯cations. Although visual and pattern-based editors of Learning Designs (LD) can help to abstract the learning designer from the details of the speci¯cations, they are still far from a high-level, integrated authoring environment. This paper analyzes the major approaches used to transform an abstract LD into a concrete unit of learning (UoL) , according to three desired features: the use of patterns and other design techniques to abstract the speci¯c representational details; the di®erence between the abstract source LD model and the concrete target UoL model; and the possibility of combining multiple models into a single environment. A classi¯cation is proposed for the LD techniques commonly found in the analyzed approaches, which we refer to as non-representational LD, in order to underline its abstraction from the details of the underlying speci¯cations. We have integrated such non-representational LD techniques in a uni¯ed Model-Driven Learning Design (MDLD) meta-modelling environment. MDLD has been used to generate units of learning from several meta-models, and tested on a IMS LD case study retrieved from the Learning Networks' knowledge base. The work concludes with a discussion on the possibilities of the new model-driven approach for learning designers.