Enriching Solution Space for Robustness in an Intelligent Tutoring System
نویسندگان
چکیده
Intelligent tutoring systems assist medical faculty in training and equipping students with the required clinical reasoning skills. Plausible student solutions to a given problem are rejected by tutoring systems as being incorrect, if they do not match a specific solution accepted by the tutoring system. This leads to brittleness in evaluating student solutions. In this paper we describe a combination of knowledge base expansion and exploitation of existing knowledge structure to enhance robustness in an intelligent tutoring system for medical problem-based learning using UMLS. We present a tutoring system that enriches the solution space by collating different plausible solutions and exploiting the knowledge structure in UMLS to offer students a broader scope of reasoning.
منابع مشابه
Expanding the Plausible Solution Space for Robustness in an Intelligent Tutoring System
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