Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.