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1. 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)

   Abstract 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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2. Design Educators’ Conceptions of Prototyping in Engineering Design Courses

   Ali, Hadi; Lande, Micah (June 2019, ASEE Annual Conference proceedings)

   This is a research study that investigates the range of conceptions of prototyping in engineering design courses through exploring the conceptions and implementations from the instructors’ perspective. Prototyping is certainly an activity central to engineering design. The context of prototyping to support engineering education and practice has a range of implementations in an undergraduate engineering curriculum, from first-year engineering to capstone engineering design experiences. Understanding faculty conceptions’ of the reason, purpose, and place of prototyping can help illustrate how teaching and learning of the engineering design process is realistically implemented across a curriculum and how students are prepared for work practice. We seek to understand, and consequently improve, engineering design teaching and learning, through transformations of practice that are based on engineering education research. In this exploratory study, we interviewed three faculty members who teach engineering design in project-based learning courses across the curriculum of an undergraduate engineering program. This builds on related work done by the authors that previously investigated undergraduate engineering students’ conceptions of prototyping activities and process. With our instructor participants, a similar interview protocol was followed through semi-structured qualitative interviews. Data analysis has been undertaken through an emerging thematic analysis of these interview transcripts. Early findings characterize the focus on teaching the design process; the kind of feedback that the educators provide on students’ prototypes; students’ behavior while working on design projects; and educators’ perspectives on the design course. Understanding faculty conceptions with students’ conceptions of prototyping can shed light on the efficacy of using prototyping as an authentic experience in design teaching and learning. In project-based learning courses, particular issues of authenticity and assessment are under consideration, especially across the curriculum. More specifically, “proportions of problems” inform “problem solving” as one of the key characteristics in design thinking, teaching and learning. More attention to prototyping as part of the study of problem-solving processes can be useful to enhance understanding of the impact of instructional design. Challenges for teaching engineering design exist, and may be due to difficulties in framing design problems, recognizing what expertise students possess, and assessing their expertise to help them reach their goals, all at an appropriate place and ambiguity with student learning goals. Initial findings show that prototyping activities can help students become more reflective on their design. Scaffolded activities in prototyping can support self-regulated learning by students. The range of support and facilities, such as campus makerspaces, may also help students and instructors alike develop industry-ready engineering students. 

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3. 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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4. This is How I Design: Discussing design principles in small multidisciplinary teams of design professionals

   Damen, N; Toh, C. (December 2020, Design Computing and Cognition)

   Gero, J. (Ed.)
5. What Distinguishes Students’ Engineering Design Performance: Design Behaviors, Design Iterations, and Application of Science Concepts

   https://doi.org/10.1007/s10956-024-10184-y  

   Du, Hanxiang; Zhu, Gaoxia; Xing, Wanli; Xie, Charles (April 2025, Journal of Science Education and Technology)

   Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors. Students were assigned to three groups based on their design performance: the achieving group, proficient group, and emerging group. By mining log activities, we explored the interactions among students’ application of science concepts, engineering design behaviors, design iterations, and their design performance. We found that the achieving group has a statistically higher number of design iterations than the other two performance groups. We also identified distinctive transition patterns in students’ applying science concepts and exercising design behaviors among three groups. The implications of this study are then discussed. 

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