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1. Developing an Understanding of Civil Engineering Practitioner Problem-solving Rationale Using Multiple Contextual Representations

   Gestson, Sean L.; Lutz, Benjamin; Brown, Shane; Barner, Matthew S.; Hurwitz, D.; Abadi, Masoud (January 2018, ASEE annual conference & exposition)

   This paper presents the preliminary findings of a larger study on the problem-solving rationale associated with the use of multiple contextual representations. Four engineering practitioners solved a problem associated with headloss in pipe flow while their visual attention was tracked using eye tracking technology. Semi-structured interviews were conducted following the problem-solving interview and the rationale associated with their decisions to use a particular contextual representation emerged. The results of this study show how the rationale can influence the problem-solving process of the four engineering practitioners. Engineering practitioners used various contextual representations and provided multiple rationale for their decisions. Eye tracking techniques and semi-structured interviews created a robust picture of the problem-solving process that supplements previous problem-solving research. 

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2. Divergent thinking in engineering: Diverse exploration is key to successful project outcomes

   Murphy, L. (July 2022, 2022 ASEE Annual Conference & Exposition)

   Engineers have the power to drive innovation and rethink the way the world is designed. However, a key practice often absent from engineering education is facilitating innovation and considering diverse perspectives through divergent thinking. We define divergence in engineering practices as exploring multiple alternatives in any stage of engineering processes. Currently, engineering education and research focuses on divergence primarily in the generation and development of design solutions, supported by idea generation methods such as Brainstorming and Design Heuristics. But in practice, there are many other opportunities throughout an engineering project where engineers may find it useful to explore multiple alternatives. When does divergent thinking take place during engineering problem solving as it is currently practiced? We conducted 90-minute semi-structured interviews with mechanical engineering practitioners working in varied setting to elicit their experiences with divergent thinking taking place in their engineering projects. The initial results document divergent thinking in six different areas of engineering design processes: 1) problem understanding, 2) problem-solving methods and strategies, 3) research and information gathering, 4) stakeholder identification, 5) considering potential solutions, and 6) anticipating implications of decisions. These findings suggest engineers find divergent thinking useful in multiple areas of engineering practice, and we suggest goals for developing divergent thinking skills in engineering education. 

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3. Understanding the Roles of Low-fidelity Prototypes in Engineering Design Activity

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

   Practical ingenuity is demonstrated in engineering design through many ways. Students and practitioners alike create many iterations of prototypes in solving problems and design challenges. While focus is on the end product and/or the process employed along the way, this study combines these interests to better understand the product and process through the roles of initial prototyping through the creation of such things as alpha prototypes, conceptual mock-ups, and other rapid prototypes. We explore the purposes and affordances of these low-fidelity prototypes in engineering design activity through both synthesis of different perspectives from literature to compose an integrated framework to characterize prototypes that are developed as part of ideation in designing, as well as historic and student examples and case studies. Studying prototyping (activity) and prototypes (artifacts) is a way to studying design thinking and how students and practitioners learn and apply a problem solving process to their work. Prototyping can make readily evident and explicit (through act of creating and the creations themselves) some of the thinking and insights of the engineering designer into the design problem. Initial, low-fidelity prototypes are characterized as prototypes that are not always elaborate depictions containing all the fine details of the design. In fact, features in a prototype do not always appear in the final design. The underpinning of this work is that prototyping, as a process, is an act of externalizing design thinking, embodying it through physical objects. While several prescriptive frameworks have been developed to describe what prototypes prototype and the role of prototype, the role of low-fidelity prototypes, specifically, lacks sufficient attention. We will present prototyping rather as an holistic mindset that can be a means to approach problem solving in a more accessible manner. It can be helpful to apply this sort of mindset approach from these initial problem understanding through functional decomposition to quickly communicate and learn by trial and building in learning loops to oneself, with an engineering design team, and to potential stakeholders outside the team. 

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4. Seeing the Forest for the Trees: Understanding Security Hazards in the 3GPP Ecosystem through Intelligent Analysis on Change Requests

   Chen, Yi; Tang D.; Yao, Y.; Zha, M.; Wang, X.; Liu, X.; Tang, H.; Zhao, D. (August 2022, Proceedings of the 31st USENIX Security Symposium)

   With the recent report of erroneous content in 3GPP specifications leading to real-world vulnerabilities, attention has been drawn to not only the specifications but also the way they are maintained and adopted by manufacturers and carriers. In this paper, we report the first study on this 3GPP ecosystem, for the purpose of understanding its security hazards. Our research leverages 414,488 Change Requests (CRs) that document the problems discovered from specifications and proposed changes, which provides valuable information about the security assurance of the 3GPP ecosystem. Analyzing these CRs is impeded by the challenge in finding security-relevant CRs (SR-CRs), whose security connections cannot be easily established by even human experts. To identify them, we developed a novel NLP/ML pipeline that utilizes a small set of positively labeled CRs to recover 1,270 high-confidence SR-CRs. Our measurement on them reveals serious consequences of specification errors and their causes, including design errors and presentation issues, particularly the pervasiveness of inconsistent descriptions (misalignment) in security-relevant content. Also important is the discovery of a security weakness inherent to the 3GPP ecosystem, which publishes an SR-CR long before the specification has been fixed and related systems have been patched. This opens an "attack window", which can be as long as 11 years! Interestingly, we found that some recently reported vulnerabilities are actually related to the CRs published years ago. Further, we identified a set of vulnerabilities affecting major carriers and mobile phones that have not been addressed even today. With the trend of SR-CRs not showing any sign of abating, we propose measures to improve the security assurance of the ecosystem, including responsible handling of SR-CRs. 

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5. Social Computational Literacy in Practice: A Framework Describing STEM Researchers’ Communication

   https://doi.org/10.1007/s41979-025-00155-2  

   Cammarota, Christian; Foster, Michael; Verostek, Mike; Patterson, Kayleigh; MacIntyre, Mikayla; Dorsey, Kimberly; Camacho-Betancourt, Andrea; Wong, Tony E; Zwickl, Benjamin (July 2025, Journal for STEM Education Research)

   Abstract Computational thinking is crucial for STEM researchers and practitioners, as it involves more than just developing skills—it is a way of thinking that enables effective problem-solving. STEM disciplines approach different problems and as such employ computational thinking uniquely, so students cannot rely solely on computer science to develop computational thinking. Less attention has been given to social aspects of computation, such as collaborating and communicating with and about computation even though social aspects are essential to problem solving. We utilized computational literacy as an alternative framework that explicitly includes social elements as a primary pillar. We conducted 15 interviews with STEM researchers to identify and organize the social aspects that play a role in their research. We organized goals by motivation (persuasion and productivity) and representation (visual and non-visual) to contextualize the use of communication in computation. We found that researchers use computation to explain research results, navigate decision making, establish rigor, ensure reproducibility, facilitate lab stability, and promote research efficiency. We used Activity Theory to describe the tools, norms, and communities associated with these goals to offer a more detailed framework for the social pillar of computational literacy within the context of science and engineering. Examples from each discipline within STEM are described. This social computational literacy framework can act as a guide for STEM educators and practitioners alike to use and teach social aspects of computation. 

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