THE EFFECTS OF TRAINING BACKGROUND AND DESIGN TOOLS ON MULTI-LEVEL BIOSYSTEMS DESIGN
Year: 2015
Editor: Christian Weber, Stephan Husung, Gaetano Cascini, Marco Cantamessa, Dorian Marjanovic, Monica Bordegoni
Author: Egan, Paul; Ho, Tiffany; Schunn, Christian; Cagan, Jonathan; LeDuc, Philip
Series: ICED
Institution: 1: Carnegie Mellon University, United States of America; 2: University of Pittsburgh, United States of America
Section: Design for Life
Page(s): 433-442
ISBN: 978-1-904670-64-3
ISSN: 2220-4334
Abstract
Biotechnologies could promote healthier lives through advancements such as complex multi-level muscle tissues. Here, cognitive processes among mechanics experts, physiology experts, and novices were investigated to determine what types of knowledge and training are beneficial. An initial hypothesis proposed that domain knowledge is not sufficient for predicting how system redesign affects performance, which was supported by all populations performing poorly on muscle redesign questions; mechanics experts outperformed other populations on force-related questions. A second hypothesis suggested that learning with multi-level design interfaces could aid participants in system redesign, which was supported by all populations performing well after training. A final hypothesis proposed that experts would excel in describing redesign effects, which was supported by expert populations describing more higher-level effects than novices, and the physiology experts suggesting the most effects on patient health. This study lays the foundation for investigating medicine and engineering in design, which has great potential for improving patient health with novel products.
Keywords: Complexity, Biomedical, Cognition, Software, Learning