MASECO: A Multi-agent System for Evaluation and Classification of OERs and OCW Based on Quality Criteria

Finding effectively open educational resources and open courseware that are the most relevant and that have the best quality for a specific user’s need, in a particular context, becomes more and more demanding. Hence, even though teachers and learners (enrolled students or self-learners as well) get to a greater extent support in finding the right educational resources, they still cannot rely on support for evaluating their quality and relevance, and, therefore, there is a stringent need for effective search and discovery tools that are able to locate high quality educational resources. We propose here a multi-agent system for evaluation and classification of open educational resources and open courseware (called MASECO) based on our socio-constructivist quality model. MASECO supports learners and instructors in their quest for the most appropriate educational resource that fulfills properly their educational needs in a given context. Faculty, educational institutions, developers, and quality assurance experts may also benefit from using it.