ccm/publications huggingface dataset:
Pulling at the Digital Thread: Exploring the Tolerance Stack Up Between Automatic Procedures and Expert Strategies in Scan To Print Processes
Authors: Tobias Mahan and Nicholas A Meisel and Christopher McComb and Jessica Menold
Published in: Journal of Mechanical Design, 2018

While the combination of 3D scanning and printing processes holds much promise for the field of new product development, problems with repeatability and accuracy have limited the wider spread adoption of some digital prototyping tools, such as 3D scanners. Studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, yet few have explored the errors in S2P processes that leverage affordable rapid noncontact scanners. Studies have yet to explore the strategies that designers, who are experienced with additive manufacturing, employ to mitigate errors. To address these gaps, a controlled study was conducted using data from 27 scans collected with a prototypical off-the-shelf noncontact optical scanner. The geometric and dimensional integrity of the digital models was found to be significantly out of tolerance at various phases of the S2P process, as compared to the original physical …
The story novice designers tell: How rhetorical structures and prototyping shape communication with external audiences
Authors: Sandeep Krishnakumar and Catherine GP Berdanier and Carlye Lauff and Christopher McComb and Jessica Menold
Published in: Design Studies 82, 2022

Communication with external audiences is a critical task within the design process. Yet, we lack fundamental knowledge about how designers communicate design solutions and decisions to such audiences. This is particularly problematic for novice designers, as without such knowledge, we cannot develop pedagogical interventions to train novices as effective communicators. In this work, we study two strategies used by novices to communicate design knowledge–argumentation and prototypes. Through a move analysis and Markov modelling, we identified six unique rhetorical moves and how novices transitioned between them. We also identified several …
Lost in Translation: Examining the Complex Relationship between Prototyping and Communication
Authors: Sandeep Krishnakumar and Catherine GP Berdanier and Christopher McComb and Jessica Menold
Published in: Journal of Mechanical Design, 2021

Prototyping plays a pivotal role in the engineering design process. Prototypes represent physical or digital manifestations of design ideas, and as such act as effective communication tools for designers. While the benefits of prototyping are well-documented in research, the fundamental ways in which the construction of a prototype affects designers' reflection on and evaluation of their design outcomes and processes are not well understood. The relationships between prototypes, designers' communication strategies, and recollection of design processes is of particular interest in this work, as preliminary research suggests that novice designers tend to struggle to clearly articulate the decisions made during the design process. This work serves to extend prior work and build foundational knowledge by exploring the evaluation of design outcomes and decisions, and communication strategies used by novice …
Assessing Engineering Design: A Comparison of the Effect of Exams and Design Practica on First-Year Students Design Self-Efficacy
Authors: Hannah Nolte and Catherine GP Berdanier and Jessica Menold
Published in: Journal of Mechanical Design, 2020

In response to calls for engineering programs to better prepare students for future careers, many institutions offer courses with a design component to first-year engineering students. This work proposes that traditional exam-based assessments of design concepts are inadequate, and alternative forms of assessment are needed to assess student learning in design courses. This paper investigates the self-efficacy differences between a traditional exam and a two-part practicum as a mid-semester assessment for introductory engineering students enrolled in a first-year design course. Increased self-efficacy has been linked to various positive student outcomes and increased retention of underrepresented students. The practicum consisted of an in-class team design task and an out-of-class individual reflection, while the exam was a traditional, individual written exam. All students completed a pre-assessment …
Towards Enhanced Creativity in Interface Design through Automated Usability Evaluation
Authors: Snehal Dhengre and Jayant Mathur and Farzaneh Oghazian and Xiaomei Tan and Christopher McComb
Published in: Proceedings of the 11th International Conference on Computational Creativity, 2020

With an increase in the number of mobile apps making their way to users, there is a growing need for tools to support the app design process. While many tools focus on increasing the pace of development, few attempt to aide the designer in generating more creative solutions. In this work, we take creativity as the combination of novelty and utility. Particularly during development of user interfaces, assessment of utility (primarily usability) is iterative, rigorous, and time-consuming. The objective of the proposed work is to explore and evaluate the use of machine learning to predict usability measures for mobile app interfaces as a means to automate usability evaluations. Specifically, a convolutional neural network (CNN) is used to accurately (nearly 90%) predict three usability measures: regularity, complexity, and touchability. This tool automates the assessment of utility in app design, freeing up the designer to seek designs that are novel and thus creative.
Collaborating with Style: Using an Agent-Based Model to Simulate Cognitive Style Diversity in Problem Solving Teams
Authors: Samuel Lapp and Kathryn Jablokow and Christopher McComb
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Collaborative problem solving can be successful or counterproductive. The performance of collaborative teams depends not only on team members’ abilities, but also on their cognitive styles. Cognitive style measures differences in problem-solving behavior: how people generate solutions, manage structure, and interact. While teamwork and problem solving have been studied separately, their interactions are less understood. This paper introduces the KAI Agent-Based Organizational Optimization Model (KABOOM), the first model to simulate cognitive style in collaborative problem solving. KABOOM simulates the performance of teams of agents with heterogeneous cognitive styles on two contextualized design problems. Results demonstrate that, depending on the problem, certain cognitive styles may be more effective than others. Also, intentionally aligning agents’ cognitive styles with their roles can improve …
An Analysis of Design Process and Performance in Distributed Data Science Teams
Authors: Torsten Maier and Joanna DeFranco and Christopher McComb
Published in: Team Performance Management, 2019

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.We specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting …
Focus and Modality: Defining A Roadmap to Future AI-Human Teaming in Design
Authors: Christopher McComb and Peter Boatwright and Jonathan Cagan
Published in: International Conference on Engineering Design, 2023

The evolution of Artificial Intelligence (AI) and Machine Learning (ML) enables new ways to envision how computer tools will aid, work with, and even guide human teams. This paper explores this new paradigm of design by considering emerging variations of AI-Human collaboration: AI used as a design tool versus AI employed as a guide to human problem solvers, and AI agents which only react to their human counterparts versus AI agents which proactively identify and address needs. The different combinations can be mapped onto a 2×2 AI-Human Teaming Matrix which isolates and highlights these different AI capabilities in teaming. The paper introduces the matrix and its quadrants, illustrating these different AI agents and their application and impact, and then provides a road map to researching and developing effective AI team collaborators.
Understanding Effects of Permafrost Degradation and Coastal Erosion on Civil Infrastructure in Arctic Coastal Villages: A Community Survey and Knowledge Co-Production
Authors: Min Liew and Ming Xiao and Louise Farquharson and Dmitry Nicolsky and Anne Jensen and Vladimir Romanovsky and Jana Peirce and Lilian Alessa and Christopher McComb and Xiong Zhang and Benjamin Jones
Published in: Journal of Marine Science and Engineering, 2022

This paper presents the results of a community survey that was designed to better understand the effects of permafrost degradation and coastal erosion on civil infrastructure. Observations were collected from residents in four Arctic coastal communities: Point Lay, Wainwright, Utqiaġvik, and Kaktovik. All four communities are underlain by continuous ice-rich permafrost with varying degrees of degradation and coastal erosion. The types, locations, and periods of observed permafrost thaw and coastal erosion were elicited. Survey participants also reported the types of civil infrastructure being affected by permafrost degradation and coastal erosion and any damage to residential buildings. Most survey participants reported that coastal erosion has been occurring for a longer period than permafrost thaw. Surface water ponding, ground surface collapse, and differential ground settlement are the three types of changes in ground surface manifested by permafrost degradation that are most frequently reported by the participants, while houses are reported as the most affected type of infrastructure in the Arctic coastal communities. Wall cracking and house tilting are the most commonly reported types of residential building damage. The effects of permafrost degradation and coastal erosion on civil infrastructure vary between communities. Locations of observed permafrost degradation and coastal erosion collected from all survey participants in each community were stacked using heatmap data visualization. The heatmaps constructed using the community survey data are reasonably consistent with modeled data synthesized from the scientific literature …
Predicting Elastic Strain Fields in Defective Microstructures using Image Colorization Algorithms
Authors: Pranav Milind Khanolkar and Christopher McComb and Saurabh Basu
Published in: Computational Material Science, 2020

In this work, an image colorization algorithm based on convolutional neural networks is explored as an approach to predict tensile plane-strain field components of microstructures featuring porosity defects. For the same, microstructures featuring porosity of various shapes, sizes, area fractions and number densities were sampled on the gage section of ASTM-E8 sized numerical specimens whose tensile deformation was simulated in plane strain mode using commercial finite element analysis package Abaqus. Subsequently, the image colorization algorithm was trained by treating the microstructure featuring porosity defects as the gray scale image, and its strain field components as its color layers, analogous to the red-green-blue color components of traditional digital representations of images. Towards the same, various CNN frameworks were tested for optimization of its parameters, viz. number of layers, number …
Modeling a Strategic Human Engineering Design Process: Human-Inspired Heuristic Guidance Through Learned Visual Design Agents
Authors: Lucas Puentes and Ayush Raina and Jonathan Cagan and Christohper McComb
Published in: International Design Conference - DESIGN 2020, 2020

Human designers often work in a visual design space, projecting step-by-step design progression through evolving mental images. The strategic evolution of that design leverages heuristics based on experience and domain knowledge. The methodology presented in this paper brings together the visual nature of design problem solving and design heuristics in a deep learning computational agent framework that emulates and enables human-mirrored design. When applied to a truss design task, results demonstrate superior results to those of human designers who provided the initial data.
The Prototyping Behaviors of Startups: Exploring the Relationship Between Prototyping Behaviors and Startup Strategies
Authors: Jacob Nelson and Tobias Mahan and Christopher McComb and Jessica Menold
Published in: Journal of Mechanical Design, 2019

Prototyping is a crucial part of new product development, and engineers and designers rely on prototyping to bring novel technologies to market. In recent years, tech-based startups like Tesla or Udacity have revolutionized their respective industries. However, many tech-based startups are unable to create a viable product with their available resources and fail before ever making it to market. In this work, we analyze survey responses from 34 startup representatives to investigate the relationship between prototyping practice, startup success, and perceived difficulty of startup tasks. K-means cluster analysis shows three distinct groups, differentiated by (1) their amount of available funding, (2) their use of prototyping best practices, and (3) their reported difficulty in startup tasks. High-performing startups reported having the highest funding, experiencing less difficulty in startup tasks, and using prototyping best …
Heuristic-Guided Solution Search through a Two-Tiered Design Grammar
Authors: Lucas Puentes and Jonathan Cagan and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2019

Grammar-based design is typically a gradual process; incremental design changes are performed until a problem statement has been satisfied. While they offer an effective means for searching a design space, standard grammars risk being computationally costly because of the iteration required, and the larger a given grammar the broader the search required. This paper proposes a two-tiered design grammar that enhances the computational design generation with generalized heuristics to provide a way to more efficiently search a design space. Specifically, this two-tiered grammar captures a combination of heuristic-based strategic actions (often observed in human designers) and smaller-scale modifications (common in traditional grammars). Rules in the higher tier are abstract and applicable across multiple design domains. Through associated guiding heuristics, these macrorules are translated down …
A Comparative Study Of Virtual Reality And Computer-Aided Design To Evaluate Parts For Additive Manufacturing
Authors: John Ostrander and Lauren Ryan and Snehal Dhengre and Christopher McComb and Timothy W Simpson and Nicholas A Meisel
Published in: ASME 2019 International Design Engineering Technical Conferences and …, 2019

Virtual Reality (VR) has been shown to be an effective assistive tool in the engineering design process, aiding designers in ergonomics studies, data visualization, and manufacturing simulation. Yet there is little research exploring the advantages of VR to assist in the design for the additive manufacturing (DfAM) process. VR may present advantages over traditional computer-aided design (CAD) tools, and these advantages may be more evident as designs become more complex. The following study investigates two types of environments: 1) Immersive Virtual Reality (VR) and 2) Non-Immersive Virtual Reality (CAD) and the advantages that each environment gives to designers to assess parts for additive manufacturing. The two environments are compared to assess potential differences in DfAM decision-making. Participants familiar with DfAM are tasked with evaluating five designs of varying complexity using …
Opportunities for Natural Language Processing in Qualitative Engineering Education Research: Two Examples
Authors: Catherine GP Berdanier and Eric Baker and Weiqin Wang and Christopher McComb
Published in: IEEE Frontiers in Education Conference, 2018

This research full paper proposes opportunities to expand qualitative and textual data analysis using natural language processing (NLP), and demonstrates opportunities to use NLP in engineering education work in our presentation of two NLP-based projects as examples of how NLP can be used. The discipline of engineering education frequently employs qualitative data analysis techniques, but fundamentally, the analysis of large corpora of textual documents is limited by researcher's time. In this paper, we present a brief review literature of how NLP has been used in other qualitative research fields. This portion of the paper is aimed to provide a clear description of NLP for those unfamiliar with machine learning and natural language processing methods. The second part of the paper will provide two brief examples of how NLP is being employed in our research group. Example 1 is a study of engineering …
Data on the Design of Truss Structures by Teams of Engineering Students
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Data in Brief, 2018

This experiment was conducted in order to compare different approaches that human teams use to solve design problems that change dynamically during solving. Specifically, study participants were given the task of designing a truss structure (similar to a bridge spanning a chasm) in teams of three. At two points during design, the problem statement was changed unexpectedly, requiring participants to adapt. Two conditions were given different initial problem representations. During the study, every participant had access to a computer interface that allowed them to construct, test, and share solutions. The interface also made it possible to collect a step-by-step log of the actions made by participants during the study. This article contains data collected from 48 participants (16 teams). This data has been used previously in behavioral analyses, sequence-based analysis, and development of computational models.
The Relationship Between Performance and Trust in AI in E-Finance
Authors: Torsten Maier and Jessica Menold and Christopher McComb
Published in: Frontiers in Artificial Intelligence, 2022

Artificial intelligence (AI) is fundamentally changing how people work in nearly every field, including online finance. However, our ability to interact with AI is moderated by factors such as performance, complexity, and trust. The work presented in this study analyzes the effect of performance on trust in a robo-advisor (AI which assists in managing investments) through an empirical investment simulation. Results show that for applications where humans and AI have comparable capabilities, the difference in performance (between the human and AI) is a moderate indicator of change in trust; however, human or AI performance individually were weak indicators. Additionally, results indicate that biases typically seen in human-human interactions may also occur in human-AI interactions when AI transparency is low.
Data on the Design and Operation of Drones by Both Individuals and Teams
Authors: Guanglu Zhang and Nicolas F Soria Zurita and Gary Stump and Binyang Song and Jonathan Cagan and Christopher McComb
Published in: Data in Brief, 2021

Human subject experiments are performed to assess the impact of artificial intelligence (AI) agents on distributed human design teams and individual human designers. In the team experiment, participants in teams of six develop and operate a drone fleet to deliver parcels routed to multiple locations of a target market. Among the design teams in the experiment, half of the design teams are human-only teams with no available AI agent. The other half of the design teams, designated as hybrid teams, have drone design and operation AI agents to advise them. Halfway through the team experiment, team structure is changed unexpectedly, requiring participants to adapt to the change. In the individual experiment, participants develop drones based on given design specifications, either on their own or with the availability of a drone design AI agent to advise them. During these experiments, participants configure, test …
Does Empathy Beget Creativity? Investigating the Role of Trait Empathy in Idea Generation and Selection
Authors: Mohammad Alsager Alzayed and Scarlett R Miller and Christopher McComb
Published in: Design Computing and Cognition’20, 437, 2020

The ability to understand and feel the needs and circumstances of others, also known as empathy, has been found to help engineering designers develop a deeper understanding of the design problems they solve. While prior work has examined the utility of empathic design experiences on driving creative concept generation, little is known about the role of a designer’s empathic tendencies in driving creative idea generation and selection in an engineering design project. Without this knowledge, we cannot be sure if, when, or how empathy influences the design process. Thus, the main goal of this paper was to identify the role of trait empathy in creative concept generation and selection in a humanitarian engineering design student project. In order to achieve this, a study was conducted with 103 first-year engineering students during three design stages of an 8-week design project (problem formulation, concept …
Natural Language Processing for Theoretical Framework Selection in Engineering Education Research
Authors: Catherine GP Berdanier and Christopher McComb and Weiwei Zhu
Published in: IEEE Frontiers in Education Conference, 2020

This research paper presents recent work exploring the power of natural language processing (NLP) methods applied to qualitative engineering education data. As NLP and other machine learning methods are developed for qualitative data, it is important to prioritize the role that theory plays in rigorous qualitative research, where the selection of a theoretical framework serves as the lens by which the research project is framed, results are analyzed, and findings are brought to light. Indeed, the view from a different theoretical lens can highlight novel or new findings. In this work, we seek to explore the viability of NLP methods for helping researchers select appropriate frameworks. In this work, we present our method to train a Python-based NLP algorithm to analyze an existing data set of interview data using one theoretical lens: Community of Practice theory, an oft-used theory in graduate education literature, which …