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|  | |  |\n|  |  |  |  |  |  |\n\nSUMMER 2015  \nAn Engineering Educator’s Decision Support Tool for Improving Innovation in Student Design Projects  \nNUR OZGE OZALTIN  \nMARY BESTERFIELD-SACRE  \nAND  \nRENEE M. CLARK University of Pittsburgh Pittsburgh, PA  \nABSTRACT  \nLearning how to design innovatively is a critical process skill for undergraduate engineers in the 21st century. To this end, our paper discusses the development and validation of a Bayesian network decision support tool that can be used by engineering educators to make recommendations that positively impact the innovativeness of product designs. Our Bayesian network model is based on Dym’s design process framework and actual design process data collected from 26 undergraduate engineering capstone teams over multiple terms. Cross validation using all available outcomes data and a sensitivity analysis showed our model to be both accurate and robust. Our model, which is based on data from teams that produced both innovative and non-innovative products, can be used to formatively assess the process used by a design team and the level of innovativeness, thereby contributing to more innovative final design outcomes.  \nKey Words: decision support tool, Bayesian network model, engineering design education  \nINTRODUCTION  \nTo capture and retain market share in the modern business environment, today’s organizations must meet or exceed customer expectations through product innovation (Shen et al. , 2000) . Innovation includes the introduction of new materials, components, and/or manufacturing processes; design changes employed to reduce manufacturing costs; and new applications of existing technology (Schumpeter, 1934) .  \nSUMMER 2015 1  \nADVANCES IN ENGINEERING EDUCATION  \nAn Engineering Educator’s Decision Support Tool for Improving Innovation in Student Design Projects  \nSolid knowledge of innovative design is critical for successful, professional contribution within the workforce; therefore, participation in student design projects such as senior capstone experiences is an integral part of the undergraduate engineering curriculum. These activities are invaluable learning experiences and equip future engineering professionals with the ability to design new products and services with innovative features in rapidly changing and highly competitive markets.  \nThus, instructors who teach innovative design techniques would benefit from a decision support tool for guiding teams to carry out those activities and processes that best contribute to the production of innovative products. To this end, our paper describes the development of such a tool that allows engineering design instructors to 1) predict the innovativeness of a design artifact given the team’s activities as well as 2) provide guidance on activities that would contribute to a more innovative design outcome. This tool, implemented as a Bayesian network model, was developed based on empirical process-level activity data collected from undergraduate teams that produced both innovative and non-innovative capstone design products. Specifically, the activities of 26 senior bioengineering design teams were journaled using a secured, online survey system over a 23 to 24 week period as they progressed from an initial design concept to a working prototype.  \nSubsequently, using this process-level activity data and Dym’s design process framework, three separate Bayesian network models for the early, middle, and late phases of the design process were built using the GeNie decision modeling software. Bayesian networks allow for determining the probability that an upstream event or activity occurred given that a downstream event, such asan innovative outcome on a capstone design project, also occurred (Genie Documentation, 2014) . Thus, with a Bayesian network and ultimately Bayes Theorem, on which this network is based, we can determ","cbCaio5EXCCgEZLA","https://ap.wps.com/l/cbCaio5EXCCgEZLA","pdf",1916238,"English","# Abstract\n# Introduction\n# Literature Review and Design Framework\n## Engineering Design and Design Frameworks","[{\"question\":\"What decision support tool does the paper develop for engineering educators?\",\"answer\":\"It develops and validates a Bayesian network decision support tool that guides educators in recommending activities that improve the innovativeness of student design projects.\"},{\"question\":\"How is the Bayesian network model constructed in the study?\",\"answer\":\"The model is based on Dym’s design process framework and empirical process-level activity data collected from 26 undergraduate engineering capstone teams.\"},{\"question\":\"How is the model validated for accuracy and robustness?\",\"answer\":\"Cross validation using available outcomes data and a sensitivity analysis are performed, showing the model to be accurate and robust.\"}]","An Engineering Educator’s Decision Support Tool for Improving Innovation in Student Design Projects - Summer 2015 | PDF",1792065671]