Design Team Convergence: The Influence of Example Solution Quality

2009 ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Semantic Analysis ◽  
Cognitive Engineering ◽  
Design Team ◽  
Design Teams ◽  
Solution Quality ◽  
Team Members ◽  
Common Understanding

This study examines how engineering design teams converge to a common understanding of a design problem and its solution, how that is influenced by the information given to them before problem solving and how it is correlated with quality of produced solutions. To understand convergence, a model of the team members’ representations was sought through a cognitive engineering design study, specifically examining the effect of the introduction of a poor example solution and a good example solution prior to problem solving. Latent Semantic Analysis (LSA) was used to track the teams’ convergence. Introducing a poor example solution was shown to have a slowing effect on teams’ convergence over time and quality of design, while the good example solution was not significantly different than the control (no example solution) in its effects on convergence, but did cause higher quality solutions. This may have implications for design team performance in practice.

Design Team Convergence: The Influence of Example Solution Quality

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Katherine Fu ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Semantic Analysis ◽  
Cognitive Engineering ◽  
Design Solution ◽  
Solution Quality ◽  
Team Members ◽  
Solution Approach ◽  
Over Time

This study examines how engineering design teams converge upon a solution to a design problem and how their solution is influenced by information given to them prior to problem solving. Specifically, the study considers the influence of the type of information received prior to problem solving on team convergence over time, as well as on the quality of produced solutions. To understand convergence, a model of the team members’ solution approach was developed through a cognitive engineering design study, specifically examining the effect of the introduction of a poor example solution or a good example solution prior to problem solving on the quality of the produced solutions. Latent semantic analysis was used to track the teams’ convergence, and the quality of design solutions was systematically assessed using pre-established criteria and multiple evaluators. Introducing a poor example solution was shown to decrease teams’ convergence over time, as well as the quality of their design solution; introducing a good example solution did not produce a statistically significant different effect on convergence compared with the control (with no prior example solution provided) but did lead to higher quality solutions.

Effect of Team Size on Problem-Solving and Solution Quality: An Empirical Study

2019 ◽  
Author(s):  
Seth Jacobs ◽  
Matthew Pfarr ◽  
Mohammad Fazelpour ◽  
Abdul Koroma ◽  
Tseday Mesfin
Keyword(s):  
Problem Solving ◽  
Empirical Study ◽  
Design Solution ◽  
Design Team ◽  
Design Teams ◽  
Team Size ◽  
Solution Quality ◽  
Team Members ◽  
Protocol Study ◽  
The Impact

Abstract The size of a team can affect how they tackle a design problem and solution quality. This paper presents a protocol study of the impact of team size on problem-solving and design solution quality. The protocols are coded with micro-strategies, and macro-strategies, and final solutions are scored using a rubric of meeting constraints, manufacturability, feasibility, and cost. The results show that the larger design team sizes analyze design solutions more frequently and propose solutions less than the smaller design teams. Among the three team sizes of 1, 3, and 5, the teams of three designers scored the best on final designs. These teams used a fair amount of both proposing solutions and analyzing solutions of micro-strategies. The teams of 5 designers use backtracking macro-strategies more frequent than teams of 3 and one because as the team size increases, more time is spent among team members to discuss previous ideas.

Understanding the Context of Multidisciplinary Design: Establishing Ecological Validity in the Study of Design Problem Solving

1993 ◽  
Vol 37 (16) ◽  
pp. 1082-1086 ◽  
Author(s):  
Michael D. McNeese ◽  
Brian S. Zaff ◽  
Clifford E. Brown ◽  
Maryalice Citera ◽  
Jonathan Selvaraj
Keyword(s):  
Problem Solving ◽  
Human Factors ◽  
Concept Mapping ◽  
Design Process ◽  
Collaborative Design ◽  
Design Problem ◽  
Design Team ◽  
Design Teams ◽  
Team Members ◽  
Design Activities

The need to understand the design process in all its complexity is motivated by an interest in the development of tools and technologies that would be capable of aiding collaborative design teams. This development effort depends upon an understanding of design activities as they occur within a real world context. Observations of design activities that are made without direct communication with the design team members may fail to capture many of the subtler aspects of the process - aspects that are best understood when described by the design team members themselves. In order to supplement observational studies, this paper presents a case study in which a dialog with members of a variety of collaborative design teams was established in order to elicit information about the nature of collaborative design. A knowledge acquisition technique, concept mapping, was used to achieve an understanding of the role of human factors specialists within the collaborative design process specific to the Air Force's system acquisition program. Results highlight various findings about the nature of design problem solving such as the way different organizational settings influence human factors input in the design process/product. The paper discusses the usefulness of concept mapping to capture in-depth design knowledge and how this type of knowledge complements other approaches to understanding design.

An Ontology-Based Competence Model for Collaborative Design

2009 ◽  
pp. 188-202
Author(s):  
Vladimir Tarasov ◽  
Kurt Sandkuhl ◽  
Magnus Lundqvist
Keyword(s):  
Problem Solving ◽  
Collaborative Design ◽  
Ontology Matching ◽  
Design Team ◽  
Design Teams ◽  
Competence Model ◽  
Design Work ◽  
Team Members ◽  
Ontology Language ◽  
Competence Models

Collaborative design in dispersed groups of engineers creates various kinds of challenges to technology, organization and social environment. This paper presents an approach to description and representation of the competences needed for a planned collaborative design project. The most important competence areas are identified starting from the nature of design work, problem solving in design teams, and working in distributed groups. The competence model is built structuring these areas according to three perspectives: general, cultural, and occupational competences. An ontological representation is proposed to implement the described model for collaborative design competence. Using an ontology language for representation of collaborative design competence models makes it possible to identify those individuals who are best suited for the collaboration by ontology matching. Furthermore, a software design team consisting of two persons was interviewed and competence profiles were created using the developed ontological representation. Modeling of the team members has confirmed that the proposed approach can be applied to modeling competences needed for collaborative design in engineering fields.

Effective Design Team Composition Using Individual and Group Cognitive Attributes

2018 ◽  
Author(s):  
Kaitlyn Fritz ◽  
Line Deschenes ◽  
Vijitashwa Pandey
Keyword(s):  
Engineering Design ◽  
Brain Activity ◽  
Design Solution ◽  
Design Team ◽  
Design Teams ◽  
Team Members ◽  
Power Spectral ◽  
Team Interactions ◽  
Significant Area ◽  
Self Assessments

Engineering design is typically a team effort. Design teams frequently need to push technical boundaries to solve the most relevant challenges faced by our society. A significant area of research across multiple fields of investigation, including engineering, is the understanding and use of an individual’s cognitive attributes in the process of assembling productive teams. This research proposes an approach to assembling an engineering design team by first defining the desirable cognitive attributes in the team members. Subsequently, based on individual cognitive profile assessments along these attributes, an exhaustive list of possible design teams is investigated based on their cumulative attribute level. We compare the performance of two teams predicted to perform at different levels, and our results verify the differences between the observations of team interactions and the quality of designs produced. In addition to self-assessments, we also investigate the brain activity of the respondents using electroencephalography (EEG) to evaluate performance in an individual and a team setting. This analysis intends to highlight the characteristics of an individuals’ brain activity under different circumstances to reveal if these characteristics contribute to the success of a design team. EEG data revealed observations such as correlation between raw amplitude and level of team contribution, a higher variation in the channel power spectral density during individual versus team tasks, and a degradation of alpha activity moving from individual to group work. The results of this research can guide organizations to form teams with the necessary cognitive attributes to achieve the optimum design solution.

A Document Analysis Method for Characterizing Design Team Performance

2003 ◽  
Vol 126 (3) ◽  
pp. 378-385 ◽  
Author(s):  
Andy Dong ◽  
Andrew W. Hill ◽  
Alice M. Agogino
Keyword(s):  
Engineering Design ◽  
Semantic Analysis ◽  
Design Teams ◽  
Team Members ◽  
Design Performance ◽  
Design Concepts ◽  
Semantic Coherence ◽  
Design Documentation ◽  
Design Documents ◽  
The Impact

The premise of this research is that the engineering design process is partially driven by achieving consensus and reconciling points of view among team members. Characterizing the quality of the design performance by measuring the coherence of the description of related design concepts and events in design documentation is examined. Latent Semantic Analysis (LSA) was used to analyze design documentation written by self-managing, cross-functional engineering design teams. Computational measurements of document variance and textual coherence were applied to the teams’ design documents, presentation materials and e-mail communication. The levels of semantic coherence were correlated to assessments by faculty and product designers and engineers from industry of the design teams’ process and outcome quality. The results indicated a statistically significant positive correlation between design document coherence and design performance, especially for poorly performing teams. The impact of this research is to provide team managers (people who create teams and manage teams) or self-organizing teams (teams that focus on self-reflection and peer evaluation) computational tools that could be integrated with design information management technologies to assist them in the management of engineering design teams.

The Influence of Team Goal Alignment and Awareness on Human-Centered Design Team Decision-Making Strategy

2021 ◽  
Author(s):  
Vivek Rao ◽  
Ananya Krishnan ◽  
Jieun Kwon ◽  
Euiyoung Kim ◽  
Alice Agogino ◽  
...  
Keyword(s):  
Decision Making ◽  
Personal Goals ◽  
Design Team ◽  
Design Teams ◽  
Goal Alignment ◽  
Team Members ◽  
Team Decision Making ◽  
Student Teams ◽  
Team Decision ◽  
Team Alignment

Abstract Design team decision-making underpins all activities in the design process. Simultaneously, goal alignment within design teams has been shown to be essential to the success of team activities, including engineering design. However, the relationship between goal alignment and design team decision-making remains unclear. In this exploratory work, we analyze six student design teams’ decision-making strategies underlying 90 selections of design methods over the course of a human-centered design project. We simultaneously examine how well each design team’s goals are aligned in terms of their perception of shared goals and their awareness of team members’ personal goals at the midpoint and end of the design process, along with three other factors underpinning team alignment at the midpoint. We report three preliminary findings about how team goal alignment and goal awareness influence team decision-making strategy that, while lacking consistent significance, invite further research. First, we observe that a decrease in awareness of team members’ personal goals may lead student teams to use a different distribution of decision-making strategies in design than teams whose awareness stays constant or increases. Second, we find that student teams exhibiting lower overall goal alignment scores appear to more frequently use agent-driven decision-making strategies, while student teams with higher overall goal alignment scores appear to more frequently use process-driven decision-making strategies. Third, we find that while student team alignment appears to influence agent- and process-driven strategy selection, its effect on outcome-driven selection is less conclusive. While grounded in student data, these findings provide a starting place for further inquiry into of designerly behavior at the nexus of teaming and design decision-making.

Integral Design: The Reflective Morphological Overview

2007 ◽  
Author(s):  
Wim Zeiler ◽  
Perica Savanovic ◽  
Emile Quanjel
Keyword(s):  
Design Process ◽  
Information Exchange ◽  
Building Design ◽  
Design Team ◽  
Design Teams ◽  
Design Decisions ◽  
Team Members ◽  
Complex Process ◽  
Dutch Association ◽  
Building Physics

Integral Building Design is done by multi disciplinary design teams and aims at integrating all aspects from the different disciplines involved in a design for a building such as; archtitecture, construction, building physics and building services. It involves information exchange between participants within the design process in amounts not yet known before. To support this highly complex process an Integral Building Design methods is developed based on the combination of a prescriptive approach, Methodical Design, and a descriptive approach, Reflective practice. Starting from the Methodical Design approach by van den Kroonenberg, a more reflective approach is developed. The use of Integral Design within the design process results in a transparency on the taken design steps and the design decisions. Within the design process, the extended prescriptive methodology is used as a framework for reflection on design process itself. To ensure a good information exchange between different disciplines during the conceptual phase of design a functional structuring technique can be used; Morphological Overviews (MO). Morphology provides a structure to give an overview of the consider functions and their solution alternatives. By using this method it is presumed that it helps to structure the communication between the design team members and a such forms a basis for reflection on the design results by the design team members. This method is used in the education program at the Technische Universiteit Eindhoven and was tested in workshops for students and for professionals from the Royal Institute of Dutch Architects (BNA) and the Dutch Association of Consulting Engineers (ONRI). Over 250 professionals participated in these workshops.

Neurophysiologic Collaboration Patterns during Team Problem Solving

2009 ◽  
Vol 53 (12) ◽  
pp. 804-808 ◽  
Author(s):  
Ron Stevens ◽  
Chris Berka ◽  
Marcia Sprang
Keyword(s):  
Problem Solving ◽  
Mental Models ◽  
Real World ◽  
Mental Workload ◽  
Team Work ◽  
Team Members ◽  
Collaboration Patterns ◽  
Random Patterns ◽  
Time Critical

We have explored using neurophysiologic collaboration patterns as an approach for developing a deeper understanding of how teams collaborate when solving time-critical, complex real-world problems. Teams of three students solved substance abuse management simulations using IMMEX software while measures of mental workload (WL) and engagement (E) were generated by electroencephalography (EEG). Levels of high and low workload and engagement were identified for each member at each epoch statistically and the vectors consisting of these measures were clustered by self organizing artificial neural networks. The resulting cognitive teamwork patterns, termed neural synchronies, were different across six different teams. When the neural synchronies were compared across the team members of individual teams segments were identified where different synchronies were preferentially expressed. Some were expressed early in the collaboration when the team members were forming mental models of the problem, others were expressed later in the collaboration when the team members were sharing their mental models and converging on a solution. These studies indicate that non-random patterns of neurophysiologic synchronies can be observed across teams and members of a team when they are engaged in problem solving. This approach may provide an approach for monitoring the quality of team work during complex, real-world and possible one of a kind problem solving.

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