Tutorial 1 (June 23)
Title of tutorial:
Semantic Web and Intelligent Learning Environments
Instructors:
Darina Dicheva : Winston-Salem State University, 601 S. Martin Luther King Jr. Drive, Winston Salem, NC 27110
Phone: +1-336-750-2484,
http://myweb.wssu.edu/dichevad/
Vania Dimitrova :
School of Computing, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK
Phone: +44-113-343-1674,
http://www.comp.leeds.ac.uk/vania/
Objectives:
The main goal of this tutorial is to present the state-of-the-art in the use of Semantic Web technologies for building Intelligent Learning Environments (ILEs). More specifically, the objectives of the proposed tutorial are:
- To outline concepts, models, languages and technologies on which the Semantic Web is grounded and to show how these technologies can be used in modern ILEs.
- To discuss recent advances in the field with a specific focus on the use of SW technologies for intelligent content creation and management, and student modelling and adaptation.
- To present hands-on experience for the participants with example SW-based intelligent educational applications.
- To discuss future directions and research challenges to Semantic-Web oriented ILEs.
It is expected that the overall benefit for the participants would be to gain an understanding of the Semantic Web technologies and their use in ILEs, which would enable them to evaluate and utilise these technologies in research and practice.
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Tutorial 2 (June 24)
Title of tutorial:
Constraint-Based Tutoring Systems: From Theory to Authoring
Instructors:
Antonija Mitrovic & Brent Martin: University of Canterbury
Private Bag 4800, Christchurch, New Zealand
Phone: (64) 3 3642987
http://www.canterbury.ac.nz/
Stellan Ohlsson : University of Illinois at Chicago, 1007 West Harrison Street, Chicago, IL 60607, USA
Phone: (312) 996-6643
http://www.uic.edu/index.html/
Objectives:
We will introduce the participants to a novel knowledge representation in which the units of knowledge are
constraints. The tutorial will touch on the differences between constraints and representations of practical
knowledge (production rules) and the standard propositional representation of declarative knowledge. Most
of the first half of the tutorial will be spent on explaining the properties of this knowledge representation
and describing and demonstrating some of its applications to date, including its use as a machine learning
algorithm and as a simulation model of human learning, but with emphasis on its use in the design of
intelligent tutoring systems.
The tutorial will consist of a mixture of lectures, simple exercises, on-line demonstrations and hands-on
activities. The presenters will take turns to present material and take participants through simple paper-and-
pencil exercises, and to demonstrate features of constraint-based systems and ASPIRE, which are available
via Web access. The presenters will also give hands-on demonstrations of systems that incorporate CBM,
and will coach the participants through a practical exercise where they will get the opportunity to build a
constraint-based system for themselves.
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