title: Method for Building a Medical Training Simulator with Bayesian Networks: SimDeCS.
abstract: Distance education has grown in importance with the advent of the internet. An adequate evaluation of students in this mode is still difficult. Distance tests or occasional on-site exams do not meet the needs of evaluation of the learning process for distance education. Bayesian networks are adequate for simulating several aspects of clinical reasoning. The possibility of integrating them in distance education student evaluation has not yet been explored much. The present work describes a Simulator based on probabilistic networks built to represent knowledge of clinical practice guidelines in Family and Community Medicine. The Bayesian Network, the basis of the simulator, was modeled to playable by the student, to give immediate feedback according to pedagogical strategies adapted to the student according to past performance, and to give a broad evaluation of performance at the end of the game. Simulators structured by Bayesian Networks may become alternatives in the evaluation of students of Medical Distance Education.
authors.affiliation: Federal University Health Science of Porto Alegre, Brazil.
authors.forename: Cecilia Dias
authors.initials: CD
authors.lastname: Flores
authors.affiliation: Federal University Health Science of Porto Alegre, Brazil.
authors.forename: João Marcelo
authors.initials: JM
authors.lastname: Fonseca
authors.affiliation: Feevale University, Brazil.
authors.forename: Marta Rosecler
authors.initials: MR
authors.lastname: Bez
authors.affiliation: Lisbon University, Portugal.
authors.forename: Ana
authors.initials: A
authors.lastname: Respício
authors.affiliation: Lisbon University, Portugal.
authors.forename: Helder
authors.initials: H
authors.lastname: Coelho
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003194:Computer-Assisted Instruction / Q000379:methods*; D004521:Educational Measurement / Q000379:methods*; D000070856:High Fidelity Simulation Training / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012984:Software