A competency—driven advisor system for multi-actor learning environments

Advising plays a central role in technology-enhanced learning (TEL). Based on a diagnosis of the activity of a learner and on relevant knowledge of this learner, an advisor system compiles and delivers some useful advices or explanations. Since 1994, we have been working on advisor systems. This paper presents the latest work on a multi-agent system that gives advice on tasks and resources based on competency-driven user models and on ontology-based multi-actor learnflows.