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.
Design thinking and computational thinking: a dual process model for addressing design problems
Abstract This paper proposes a relationship between design thinking and computational thinking. It describes design thinking and computational thinking as two prominent ways of understanding how people address design problems. It suggests that, currently, each of design thinking and computational thinking is defined and theorized in isolation from the other. A two-dimensional ontological space of the ways that people think in addressing problems is proposed, based on the orientation of the thinker towards problem and solution generality/specificity. Placement of design thinking and computational thinking within this space and discussion of their relationship leads to the suggestion of a dual process model for addressing design problems. It suggests that, in this model, design thinking and computational thinking are processes that are ontological mirror images of each other, and are the two processes by which thinkers address problems. Thinkers can move fluently between the two. The paper makes a contribution towards the theoretical foundations of design thinking and proposes questions about how design thinking and computational thinking might be both investigated and taught as constituent parts of a dual process.
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- Award ID(s):
- 1762415
- PAR ID:
- 10253719
- Date Published:
- Journal Name:
- Design Science
- Volume:
- 7
- ISSN:
- 2053-4701
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Design thinking is a robust framework for creatively and effectively identifying and solving important human problems. While design thinking is commonly associated with fields like industrial design, it can be applied to many problem types. For example, several recent examples demonstrate the applicability of design thinking to the design and development of educational materials, courses, and systems. These results suggest that design thinking could be used as a framework to (re)design and develop effective engineering courses. The goal of this project is to understand how nine educators from different backgrounds did or did not use design thinking to redesign a sophomore-level electrical and computer engineering course. The primary source of data was 21 transcribed audio recordings of design meetings and is supplemented with interviews, reflections, and course artifacts. Thematic analysis revealed 10 themes that represent connections and disconnections between the process used and a common five-stage design thinking process (empathize, define, ideate, prototype, and test). These themes demonstrate some of the opportunities and challenges related to design thinking within an engineering course design setting. In particular, they suggest that engineering course design is a relevant context for design thinking, but one to which design thinking methods do not always naturally translated. Future work should focus on better understanding unique applications of design thinking within engineering course design and methods that might to support more designerly behaviors among engineering educators.more » « less
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Three broad issues have been identified in the professional formation of engineers: 1) the gap between what students learn in universities and what they practice upon graduation; 2) the limiting perception that engineering is solely technical, math, and theory oriented; and 3) the lack of diversity (representation of a wide range of people) and lack of inclusion (incorporation of different perspectives, values, and ways of thinking and being in engineering) in many engineering programs. These are not new challenges in engineering education, rather they are persistent and difficult to change. There have been countless calls to recruit and retain women and underrepresented minority group members into engineering careers and numerous strategies proposed to improve diversity, inclusion, and retention, as well as to calls to examine socio-technical integration in engineering cultures and education for professional formation. Despite the changes in some disciplinary profiles in engineering and the curricular reforms within engineering education, there still has not been the deep transformation needed to integrate inclusionary processes and thinking into professional formation. In part, the reason is that diversity and inclusion are still framed as simply “numbers problems” to be solved. What is needed instead is an approach that understands and explores diversity and inclusion as interrelated with the epistemological (what do engineers need to know) and ontological (what does it mean to be an engineer) underpinnings of engineering. These issues are highly complex, interconnected, and not amenable to simple solutions, that is, they are “wicked” problems. They require design thinking. Thus our NSF-funded Research in the Formation of Engineers (RFE) study utilizes a design thinking approach and research activities to explore foundational understandings of formation and diversity and inclusion in engineering while addressing the three project objectives: 1) Better prepare engineers for today’s workforce; 2) Broaden understandings of engineering practice as both social and technical; and 3) Create and sustain more diverse and inclusionary engineering programs. The project is organized around the three phases of the design process (inspiration, ideation, and implementation), and embedded within the design process is a longitudinal, multiphase, mixed-methods study. Although the goal is to eventually study these objectives on a broader scale, we begin with a smaller context: the School of Electrical and Computer Engineering (ECE) and the Weldon School of Biomedical Engineering (BME) at Purdue University. These schools share similarities with some common coursework and faculty, but also provide contrasts as BME’s undergraduate population, on average for recent semesters, has been 44-46% female, where ECE has been 13-14% female. Although BME has slightly more underrepresented minority students (7-8% versus 5%), approximately 60% of BME students are white, versus 40% for ECE. It is important to note that Purdue’s School of ECE offers B.S. degrees in Electrical Engineering (EE) and Computer Engineering (CmpE), which reflect unique disciplinary cultures. Additionally, the schools differ significantly on undergraduate enrollment. The BME enrollment was 278, whereas ECE’s enrollment was 675 in EE and 541 in CmpE1. In this paper we describe the background literature and the research design, including the study contexts, target subject populations, and procedures for quantitative and qualitative data collection and analysis. In addition, we present the data collected during the first phase of the research project. In our poster, we will present preliminary analysis of the first phase data.more » « less
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Computational thinking has widely been recognized as a crucial skill for engineers engaged in problem-solving. Multidisciplinary learning environments such as integrated STEM courses are powerful spaces where computational thinking skills can be cultivated. However, it is not clear the best ways to integrate computational thinking instruction or how students develop computational thinking in those spaces. Thus, we wonder: To what extent does engaging students in integrated engineering design and physics labs impact their development of computational thinking? We have incorporated engineering design within a traditional introductory calculus-based physics lab to promote students’ conceptual understanding of physics while fostering scientific inquiry, mathematical modeling, engineering design, and computational thinking. Using a generic qualitative research approach, we explored the development of computational thinking for six teams when completing an engineering design challenge to propose an algorithm to remotely control an autonomous guided vehicle throughout a warehouse. Across five consecutive lab sessions, teams represented their algorithms using a flowchart, completing four iterations of their initial flowchart. 24 flowcharts were open coded for evidence of four computational thinking facets: decomposition, abstraction, algorithms, and debugging. Our results suggest that students’ initial flowcharts focused on decomposing the problem and abstracting aspects that teams initially found to be more relevant. After each iteration, teams refined their flowcharts using pattern recognition, algorithm design, efficiency, and debugging. The teams would benefit from having more feedback about their understanding of the problem, the relevant physics concepts, and the logic and efficiency of the flowchartsmore » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1017/dsj.2021.7
