A Competence-Based Course Authoring Concept for Learning Platforms with Legacy Assignment Tools

This paper is concerned with several of the most important aspects of Competence-Based Learning (CBL): course authoring, assignments, and categorization of learning content. The latter is part of the so-called Bologna Process (BP) and can effectively be supported by integrating knowledge resources like, e.g., standardized skill and competence taxonomies into the target implementation approach, aiming at making effective use of an open integration architecture while fostering the interoperability of hybrid knowledge-based e-learning solutions. Modern scenarios ask for interoperable software solutions to seamlessly integrate existing e-learning infrastructures and legacy tools with innovative technologies while being cognitively efficient to handle. In this way, prospective users are enabled to use them without learning overheads. At the same time, methods of Learning Design (LD) in combination with CBL are getting more and more important for production and maintenance of easy to facilitate solutions. We present our approach of developing a competence-based course-authoring and assignment support software. It is bridging the gaps between contemporary Learning Management Systems (LMS) and established legacy learning infrastructures by embedding existing resources via Learning Tools Interoperability (LTI). Furthermore, the underlying conceptual architecture for this integration approach will be explained. In addition, a competence management structure based on knowledge technologies supporting standardized skill and competence taxonomies will be introduced. The overall goal is to develop a software solution which will not only flawlessly merge into a legacy platform and several other learning environments, but also remain intuitively usable. As a proof of concept, the so-called platform independent conceptual architecture model will be validated by a concrete use case scenario.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.