A general architecture for intelligent tutoring of diagnostic classification problem solving.
作者： Olga Medvedeva , Rebecca S. Crowley
DOI:
关键词: Domain (software engineering) 、 Ontology 、 Computer science 、 Ontology (information science) 、 Architecture 、 Cognition 、 Intelligent tutoring system 、 Component (UML) 、 Reuse 、 Artificial intelligence 、 Set (abstract data type) 、 Graph (abstract data type)
摘要: We report on a general architecture for creating knowledge-based medical training systems to teach diagnostic classification problem solving. The approach is informed by our previous work describing the development of expertise in solving Pathology. envelops traditional Intelligent Tutoring System design within Unified Problem-solving Method description Language (UPML) architecture, supporting component modularity and reuse. Based domain ontology, task ontology case data, abstract problem-solving methods expert model create dynamic solution graph. Student interaction with graph filtered through an instructional layer, which created second set pedagogic ontologies, response current state student model. outline advantages limitations this approach, describe it's implementation SlideTutor - developing Dermatopathology.
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