Employing UMLS for generating hints in a tutoring system for medical problem-based learning

摘要

While problem-based learning has become widely popular for imparting clinical reasoning skills, the dynamics of medical PBL require close attention to a small group of students, placing a burden on medical faculty, whose time is over taxed. Intelligent tutoring systems (ITSs) offer an attractive means to increase the amount of facilitated PBL training the students receive. But typical intelligent tutoring system architectures make use of a domain model that provides a limited set of approved solutions to problems presented to students. Student solutions that do not match the approved ones, but are otherwise partially correct, receive little acknowledgement as feedback, stifling broader reasoning. Allowing students to creatively explore the space of possible solutions is exactly one of the attractive features of PBL. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that leverages a domain ontology to provide effective feedback. The concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards a correct solution. We describe the strategy incorporated in METEOR, a tutoring system for medical PBL, wherein the widely available UMLS is deployed and represented as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (Spearman's ρ=0.80, p<0.01). Hints containing partial correctness feedback scored significantly higher than those without it (Mann Whitney, p<0.001). Hints produced by a human expert received an average score of 4.2 (Spearman's ρ=0.80, p<0.01).