Peticion de Articulos

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

Tipos de contribuciones
Paper Types Long papers
Up to 8 pages
Paper Types Short papers
Up to 4 pages
Paper Types Posters
Up to 2 pages

Areas de Interes de la Conferencia
Las areas de interes incluyen pero no estan limitadas a:
Topic Building networks: using expert's opinion, learning from data, theoretical models, mixed approaches
Topic User modeling: cognitive diagnosis, metacognitife features, affective states, emotional monitoring, multi-layered models.
Topic Practical barriers to adoption
Topic Displaying results to users: inspectable models, open models, etc.
Topic Reusability, maintenance and upgrade of systems
Topic Task selection algorithms: curriculum sequencing, testing knowledge, problem selection, etc.

Presentacion de Articulos

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.

Instrucciones para enviar la version final To be announced....

Fechas Límite

[Peticion de Articulos] [Fechas Importantes] [Enviar Articulos] [Principal] [Comite de Programa]