METEOR: medical tutor employing ontology for robustness

  • Authors:

  • Hameedullah Kazi;Peter Haddawy;Siriwan Suebnukarn

  • Affiliations:

  • Isra University, Hyderabad, Pakistan;United Nations University, Macau, Macao;Thammasat University, Klong Luang, Thailand

  • Venue:
  • Proceedings of the 16th international conference on Intelligent user interfaces
  • Year:
  • 2011

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Abstract

Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical PBL can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report a tutoring system for medical PBL that employs the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the ontology to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p