Adaptive Dialogues for Learning Radiology
Abstract
This paper describes how cognitive and computational concepts can be applied to build interface and learner models for long-term tutorial interactions in medical Radiology. Key human-to-human tutorial dialogue factors that typically occur in the different stages of skill acquisition are captured through an empirical study. The results of the study are detailed and linked to the design of RUI-2, an Authoring and Intelligent Tutoring System shell for multiple domains of radiological expertise. A brief discussion and future research directions offer a comparative view of the method and tools.References
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M. Estep, editor (2006). Self-Organizing Natural Intelligence: Issues of Knowing, Meaning, and Complexity. Springer, New York.
M. Sharples, N. Jeffery, B. du Boulay, B. Teather, D. Teather, and G. du Boulay. (2000). Structured computer-based training in the interpretation of neuroradiological images. International Journal of Medical Informatics, 60(3):263. 280.
S. Ainsworth, (2006) DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16 (3), 183-198.
R. Power, D. Scott (1998). Multilingual authoring using feedback texts. In Proceedings of the 17th International Conference on Computational Linguistics (COLING/ACL-98), pag. 1053-1059.
D. Cury, N. Omar, A. Direne (1998). Modelos baseados em estereótipos e oráculos para a aprendizagem de conceitos visuais. Revista Brasileira de Informática na Educação, v. 2, pp 43-53, 1998.
K. Luchini, C. Quintana, and E. Soloway (2004). Design guidelines for learner-centered handheld tools. In Proc. of the ACM Conference on Human Factors in Computing Systems (CHI2004), pp. 24.29.
S. Bull and J. Kay (2007). Student models that invite the learner in: The SMILI open learner modelling framework. International. Journal of Artificial Intelligence in Education, 17(2):89-120.
S. Lam, J. Pan, D. Sleeman, and W. Vasconcelos (2006). A finegrained approach to resolving unsatisfiable ontologies. In Proc. of the IEEE/WIC/ACM International Conference on Web Intelligence (WI-2006).
I. Arroyo, T. Murray, B. Woolf, and C. Beal (2004). Inferring hidden learning variables from student help seeking behavior. In Proc. of the 7th International Conference on Intelligent Tutoring Systems (ITS2004), pages 782.784. Springer.
P. Gott and A. Lesgold (2000). Competence in the workplace: How cognitive performance models and situated instruction can accelerate skill acquisition. In R. Glaser, editor, Advances ininstructional Psychology. Lawrence Erlbaum.
A. Direne, D. Scott (2001). Identifying the component features of expertise in domains of complex visual recognition. Relatório Técnico ITRI-01-23, Information Technology Research Institute, University of Brighton - Inglaterra.
M. Estep, editor (2006). Self-Organizing Natural Intelligence: Issues of Knowing, Meaning, and Complexity. Springer, New York.
Published
2008-07-12
How to Cite
DIRENE, Alexandre; SUNYE, Marcos; CASTILHO, Marcos; SILVA, Fabiano; BONA, Luis; GARCÍA, Laura; SCOTT, Donia.
Adaptive Dialogues for Learning Radiology. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 8. , 2008, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2008
.
p. 81-90.
ISSN 2763-8952.
