1. ACHIEVEMENTS OF LEARNING DESIGN IN MULTI-AGENT MULTILANGUAGE INTELLIGENT SYSTEMS – THE I-TUTOR APPROACH Dénes Zarka BME BUDAPEST 14 June 2013. Oslo

2. History of the BME 1782 -1850Institutum Geometricum - Hydrotechnicum 1846 - 1856 Joseph College of Technology 1856 - 1871Royal Joseph Polytechnic 1871 - 1934Royal Joseph University 1934 - 1949 Royal Joseph Technical and Economical University in Hungary 1949 -2000Technical University of Budapest 2000 - University of Technology and Economics

3. 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)

4. 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

5. Multi-agent, multi-language Intelligent Systems Educational robots Multi-function (more than one agent) Multi-language Multi-purpose Multi-domain Single platform

6. Whom to support? Designer –Design process –Domain –Instructional process Tutor –Domain –Learner behaviour –Tuition process Learner –Domain –Learning process

7. What agents?

8. Research in IISD Intellingent Instructional Systems Design (ADDIE) Old school – Wants to solve the problem theoretically Agent 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

9. 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

10. Semantic support Stemming, Stop-words removal, Keywords extraction, Topic categorization, NER, Latent Semantic Indexing.

11. Other agents Chatbot Alerting agent Profiling agent

12. http://edu-inno.bme.hu