Bayesian Networks in Intelligent Tutoring
26/6/2006 - 26/6/2006

<FONT COLOR=red>Conference site</FONT>

Personalization and adaptivity are invaluable features in modern intelligent tutoring systems. However, it is not easy for software to decide how to adapt its performance to meet individual student's needs, partially because usually the uncertain nature of the correspondences between what we observe and what we need to know for making the best decisions for intelligent tutoring. For instance, students that are competent in the subject matter being tested can make unintentional mistakes. It is also difficult to infer students' learning needs by observing how they explore educational web sites. Building operational models that can capture and reason about the uncertainty is thus important for intelligent tutoring that aims at meeting individual student's needs.

Bayesian networks offer a great formalism for modeling under uncertainty, and have been adopted in many applications including intelligent tutoring in the past decade or so. This satellite workshop of the Eighth International Conference on Intelligent Tutoring Systems aims at providing a forum for interested researchers and practitioners to discuss and share methodologies and applications of Bayesian networks in all aspects of intelligent tutoring.

Topics of interest include, but are not limited to:
  • Building networks: using expert's opinion, learning from data, theoretical models, etc.
  • User modeling: diagnosis of knowledge/skills, multi-layered models, affective computing, emotional monitoring, etc.
  • Task selection algorithms: curriculum sequencing, testing knowledge, problem selection, etc.
  • Displaying results to users: inspectable models, open models, etc.
  • Reusability, maintenance and upgrade of systems
  • Practical barriers to adoption

[Call for papers] [Important Dates] [Submission] [Main] [Program Committee]