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