GRAPPLE (Generic Responsive Adaptive Personalized Learning Environment)

A common abstract description of desired adaptive behaviour, using conceptual adaptation models (or CAMs), and a “translation” to lower level adaptation rules that can be used by actual adaptation engines.

A general purpose adaptation engine that can adapt any (xml) information, either as part of a pipeline from source to user-interface, or as a stand-alone adaptive learning environment that performs adaptation and complete user-interface presentation.

User modelling (or UM) services that keep track of each user’s learning process in order to provide input to the adaptation engine to base the adaptation on.

A distributed user modelling (UM) architecture will be designed and developed to link different ALEs and UM services together, and to perform retrieval and reasoning over UM information coming from different services, as well as reason about where to send updates to a user model to.

The adaptive functionality will be integrated in different existing learning management systems, either open source ones (Moodle, Claroline, Sakai) or proprietary systems aimed at corporate learning applications (realized by GRAPPLE’s industry partners).

An evaluation framework will be set up and used extensively to evaluate the usability and effectiveness of adding adaptive behaviour to learning applications, in higher education and in corporate settings.

Documentation and training material, mostly for authors of adaptive learning material, will be developed and used during training sessions for educators willing to introduce adaptive TEL in their organization and to participate in the GRAPPLE evaluation process.

Authoring tools to define CAMs, capturing relationships between concepts that are of a navigational and/or pedagogical nature, e.g. prerequisite relationships. In order to “package” a learning application consisting of domain-dependent information and CAMs extensions to standards will be proposed that can represent all required information to port learning applications between different adaptive learning environments (or ALEs).