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ACHIEVEMENTS OF LEARNING DESIGN IN MULTI-AGENT MULTILANGUAGE
INTELLIGENT SYSTEMS – THE I-TUTOR APPROACH
Dénes Zarka BME BUDAPEST14 June 2013. Oslo
History of the BME
• 1782 -1850 Institutum Geometricum -Hydrotechnicum
• 1846 - 1856 Joseph College of Technology• 1856 - 1871 Royal Joseph Polytechnic • 1871 - 1934 Royal Joseph University • 1934 - 1949 Royal Joseph Technical and
Economical University in Hungary
• 1949 -2000 Technical University of Budapest
• 2000 - University of Technology and Economics
The presenter
• Electrical Engineer 48 (graduated ’89 BME)
• From ‘92 instructional designer• Till ’98 Budapest Training Technology
Center• From ‘98 BME Learing Innovation
Center• Course develpmpnet content
development, educational research, training of designers and tutors (TEL)
The Centre
• The ‘Distance Learning Centre’ opened in 1998• 2003: Distance and Adult Learning Centre• The Distance and Adult learning Centre is continuing
to accomplish its mission with a new name: Centre for Learning Innovation and Adult Learning since 2007 as a unit of Applied Pedagogy and Psychology Institute
• The Centre is hosting EDEN (European Distance and E-learning Network) secretariat since 1997, which moved to Budapest from the British Open University
Multi-agent, multi-language Intelligent Systems
• Educational robots• Multi-function (more
than one agent)• Multi-language• Multi-purpose• Multi-domain• Single platform
Whom to support?• Designer
– Design process– Domain– Instructional process
• Tutor– Domain– Learner behaviour– Tuition process
• Learner– Domain– Learning process
What agents?
Research in IISD
Intellingent Instructional Systems Design (ADDIE)• Old school – Wants to solve the problem theoreticallyAgent model – ID model classical maching (agent-
learner)• Long history: Pedagogical agent, Taxonomy agents,
authoring agents (LDSE)• Ontological Agents• Semantical web (XML, OWL, LSA)• Standard vocabularies in ID
Learning design agent
• Learning design process: – Modules, sessions, activities
• Didactical device: – Tool – title, people, time -> Learning path– Content – subject, objectives, finalities -> Content path
• Desinging steps:– Modul design with macro objectives, description,
keywords– Session design with micro objectives and activities
Semantic support• Stemming,• Stop-words
removal,• Keywords
extraction,• Topic
categorization,• NER,• Latent Semantic
Indexing.
Other agents
• Chatbot• Alerting agent• Profiling agent
http://edu-inno.bme.hu