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1. Bridging the design-science gap with tools: Science learning and design behaviors in a simulated environment for engineering design: BRIDGING THE DESIGN-SCIENCE GAP

   https://doi.org/10.1002/tea.21398  

   Chao, Jie ; Xie, Charles ; Nourian, Saeid ; Chen, Guanhua ; Bailey, Siobhan ; Goldstein, Molly H. ; Purzer, Senay ; Adams, Robin S. ; Tutwiler, M. Shane ( October 2017 , Journal of Research in Science Teaching)
2. An Exploration of Course Design Heuristics Identified from Design Meetings, Design Artifacts, and Educator Interviews

   https://doi.org/10.18260/1-2--32060  

   Fila, Nicholas ; McKIlligan, Seda ( June 2019 , 2019 ASEE Annual Conference)

   This research paper investigates differences between course design heuristics that have been identified from three distinct data sources: course design team meetings, educator interviews, and course design papers. The study of heuristics used by experts in a discipline can have several practical benefits. They can (1) be employed as tools to scaffold expert behavior among novices, (2) be translated into processes to make challenging tasks more efficient, and (3) provide deeper insights into the nature of a domain, task, or discipline. While the study of heuristics remains robust across domains, they have demonstrated differences in format and have been identified through a variety of data types. The purpose of this study is to unpack differences in heuristics independently identified through different data types in order to better understand the role these types of data can play in understanding of heuristics for course design, especially as related to engineering courses. We utilized thematic analysis to explore the patterns of differences between heuristics identified from the three settings in three related, but distinct studies. Datasets includes audio-recordings from a four-month team course redesign process, five approximately hour-long educator interviews, and 183 peer-reviewed course design papers. We identified four themes representing differences across the datasets: (1) differences in volume/frequency of heuristics, (2) differences in breadth, specificity, and conceptualizations evidenced by categories of heuristics, (3) individual heuristic specificity, and (4) locus of clarity in heuristic examples. These results inform a set of four considerations for selecting data sources for studies of heuristics within engineering course design and other domains. 

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3. Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors

   https://doi.org/10.1115/DETC2021-72406  

   Hafizur Rahman, M. ; Xie, C. ; Sha, Z. ( November 2021 , ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.

    

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4. Bringing Design to the Privacy Table: Broadening “Design” in “Privacy by Design” Through the Lens of HCI

   https://doi.org/10.1145/3290605.3300492  

   Wong, Richmond Y. ; Mulligan, Deirdre K. ( May 2019 , CHI conference on human factors in computing systems)

   null (Ed.)

   In calls for privacy by design (PBD), regulators and privacy scholars have investigated the richness of the concept of "privacy." In contrast, "design" in HCI is comprised of rich and complex concepts and practices, but has received much less attention in the PBD context. Conducting a literature review of HCI publications discussing privacy and design, this paper articulates a set of dimensions along which design relates to privacy, including: the purpose of design, which actors do design work in these settings, and the envisioned benefciaries of design work. We suggest new roles for HCI and design in PBD research and practice: utilizing values-and critically-oriented design approaches to foreground social values and help defne privacy problem spaces.We argue such approaches, in addition to current "design to solve privacy problems" eforts, are essential to the full realization of PBD, while noting the politics involved when choosing design to address privacy. 

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5. Bayesian Network Structure Optimization for Improved Design Space Mapping for Design Exploration With Materials Design Applications

   https://doi.org/10.1115/DETC2017-67643  

   Sharpe, Conner ; Morris, Clinton ; Goldsberry, Benjamin ; Seepersad, Carolyn Conner ; Haberman, Michael R. ( August 2017 , Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Modern design problems present both opportunities and challenges, including multifunctionality, high dimensionality, highly nonlinear multimodal responses, and multiple levels or scales. These factors are particularly important in materials design problems and make it difficult for traditional optimization algorithms to search the space effectively, and designer intuition is often insufficient in problems of this complexity. Efficient machine learning algorithms can map complex design spaces to help designers quickly identify promising regions of the design space. In particular, Bayesian network classifiers (BNCs) have been demonstrated as effective tools for top-down design of complex multilevel problems. The most common instantiations of BNCs assume that all design variables are independent. This assumption reduces computational cost, but can limit accuracy especially in engineering problems with interacting factors. The ability to learn representative network structures from data could provide accurate maps of the design space with limited computational expense. Population-based stochastic optimization techniques such as genetic algorithms (GAs) are ideal for optimizing networks because they accommodate discrete, combinatorial, and multimodal problems. Our approach utilizes GAs to identify optimal networks based on limited training sets so that future test points can be classified as accurately and efficiently as possible. This method is first tested on a common machine learning data set, and then demonstrated on a sample design problem of a composite material subjected to a planar sound wave. 

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