Year: 2011
Type: conference-paper
Abstract: A variety of computer systems called virtual patients are available in medical education today. Virtual patients are designed to emulate realistic clinical cases on a computer, and help students to practice diagnosis and clinical reasoning. They are used as an integral part of the curriculum in many medical schools. However, the technologies currently used to build virtual patients present limitations. Feedback has to be edited manually by medical experts, and the feedback provided is often not adapted to each student's interactions with the virtual patient. This makes creating and editing a virtual patient time-consuming, and limits its pedagogical impact. Indeed, relevant feedback is crucial to help students assess and reflect on their performance, reflect on their decisions and improve their clinical reasoning skills. This paper presents research on automatic feedback generation for virtual patients, using semantic web technologies. To generate feedback, a computer model has been designed to represent virtual patients and students’ interactions, using semantic web technologies. The use of semantic web technologies allows a computer readable connection between medical conditions, their symptoms and the corresponding examinations. Some of these connections can be pulled from existing linked data available on the web, which would facilitate the creation and maintenance of virtual patient data. A survey has been conducted to determine the most useful types of feedback for medical students. Relating this encoded knowledge to data describing the student’s choices of examinations allows the automatic generation of such feedback in virtual patients.
Authors: Jean-Rémy Duboc, Mark Weal, Hugh Davis, Sunhea Choi
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W1522055710