| Resumen |
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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
there are rare deterministic correspondences between what we observe
and what we need to know for making the best decisions for intelligent
tutoring. For instance, students can make unintentional mistakes while
they are competent in the subject matter being tested. 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 sound approach 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: methods for
learning Bayesian networks of interest either from experts, data, or
both; application of Bayesian networks to modeling participants in the
learning process, including students and teachers; how the models can
help us choose pedagogical activities, including student assessment,
course material presentation, task sequencing, problem selection; and
practical issues such as reusability, maintenance, and scalability of
the resulting tutoring assistance systems.
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| Tipos de contribuciones |
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Long papers
Up to 8 pages |
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Short papers
Up to 4 pages |
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Posters
Up to 2 pages |
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| Areas de Interes de la Conferencia |
| Las areas de interes incluyen pero no estan limitadas a: |
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Building networks: using expert's opinion, learning from
data, theoretical models, mixed approaches
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User modeling: cognitive diagnosis, metacognitife features, affective states, emotional monitoring, multi-layered models.
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Practical barriers to adoption
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Displaying results to users: inspectable models, open
models, etc.
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Reusability, maintenance and upgrade of systems
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Task selection algorithms: curriculum sequencing, testing
knowledge, problem selection, etc.
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| Presentacion de Articulos |
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The
format for the submissions is provided by the ITS WS doc template or ITS WS Latex template
Submissions should be sent in pdf
format.
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