Affiliations: Computer Science & Information Management Program,
Asian Institute of Technology, Pathumthani 12120, Thailand.
E-mail: [email protected] | Department of Computer Science, Isra University,
Hala Road, Hyderabad, Pakistan. E-mail: [email protected] | School of Dentistry, Thammasat University, Pathumthani
12121, Thailand. E-mail: [email protected]
Abstract: In well-defined domains such as Physics, Mathematics, and Chemistry,
solutions to a posed problem can objectively be classified as correct or
incorrect. In ill-defined domains such as medicine, the classification of
solutions to a patient problem as correct or incorrect is much more complex.
Typical tutoring systems accept only a small set of approved solutions for each
problem scenario fed to the system. Plausible student solutions that fall
outside the scope of this small set of approved solutions are rejected as being
incorrect, even though these solutions may be acceptable or close to
acceptable. This leads to brittleness in the evaluation of student solutions.
This paper describes a tutoring system for medical problem-based learning
(PBL), which can accept a wide variety of plausible solutions without placing
an extensive burden on knowledge acquisition. A widely available medical
knowledge source is deployed as a domain ontology, and concept relationships in
the ontology are used to make inferences and expand the space of plausible
solutions beyond the scope of solutions explicitly provided to the system.
Parent-child relationships are used to infer generalized solutions, whereas
relationships of synonymy are used to infer alternate solutions. Evaluations of
the system after expanding the solution space indicate accuracy close to that
of human experts, who agreed among themselves with Pearson Correlation
Coefficient of 0.48 and p < 0.05. The system precision dropped by 32%, while
the recall increased by five times. The geometric mean of sensitivity and specificity was increased by 0.33.
Keywords: Robustness, ill-defined domains, medical PBL, UMLS, knowledge acquisition, ITS
