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.

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.

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.

scholarly journals The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output

2012 ◽  
Author(s):  
Katherine Fu ◽  
Joel Chan ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky ◽  
Christian Schunn ◽  
...  
Keyword(s):  
Engineering Design ◽  
Design Process ◽  
Design Problem ◽  
Semantic Analysis ◽  
Cognitive Engineering ◽  
Design Solution ◽  
Data Mapping ◽  
Design Study ◽  
Design By Analogy ◽  
The Impact

This work lends insight into the meaning and impact of “near” and “far” analogies. A cognitive engineering design study is presented that examines the effect of the distance of analogical design stimuli on design solution generation, and places those findings in context of results from the literature. The work ultimately sheds new light on the impact of analogies in the design process and the significance of their distance from a design problem. In this work, the design repository from which analogical stimuli are chosen is the U.S. patent database, a natural choice, as it is one of the largest and easily accessed catalogued databases of inventions. The “near” and “far” analogical stimuli for this study were chosen based on a structure of patents, created using a combination of Latent Semantic Analysis and a Bayesian based algorithm for discovering structural form, resulting in clusters of patents connected by their relative similarity. The findings of this engineering design study are contextualized with the findings of recent work in design by analogy, by mapping the analogical stimuli used in the earlier work into similar structures along with the patents used in the current study. Doing so allows the discovery of a relationship between all of the stimuli and their relative distance from the design problem. The results confirm that “near” and “far” are relative terms, and depend on the characteristics of the potential stimuli. Further, although the literature has shown that “far” analogical stimuli are more likely to lead to the generation innovative solutions with novel characteristics, there is such a thing as too far. That is, if the stimuli are too distant, they then can become harmful to the design process. Importantly, as well, the data mapping approach to identify analogies works, and is able to impact the effectiveness of the design process. This work has implications not only in the area of finding inspirational designs to use for design by analogy processes in practice, but also for synthesis, or perhaps even unification, of future studies in the field of design by analogy.

scholarly journals The Meaning of “Near” and “Far”: The Impact of Structuring Design Databases and the Effect of Distance of Analogy on Design Output

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Katherine Fu ◽  
Joel Chan ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky ◽  
Christian Schunn ◽  
...  
Keyword(s):  
Engineering Design ◽  
Design Process ◽  
Design Problem ◽  
Semantic Analysis ◽  
Cognitive Engineering ◽  
Design Solution ◽  
Data Mapping ◽  
Design Study ◽  
Design By Analogy ◽  
The Impact

This work lends insight into the meaning and impact of “near” and “far” analogies. A cognitive engineering design study is presented that examines the effect of the distance of analogical design stimuli on design solution generation, and places those findings in context of results from the literature. The work ultimately sheds new light on the impact of analogies in the design process and the significance of their distance from a design problem. In this work, the design repository from which analogical stimuli are chosen is the U.S. patent database, a natural choice, as it is one of the largest and easily accessed catalogued databases of inventions. The “near” and “far” analogical stimuli for this study were chosen based on a structure of patents, created using a combination of latent semantic analysis and a Bayesian based algorithm for discovering structural form, resulting in clusters of patents connected by their relative similarity. The findings of this engineering design study are juxtaposed with the findings of a previous study by the authors in design by analogy, which appear to be contradictory when viewed independently. However, by mapping the analogical stimuli used in the earlier work into similar structures along with the patents used in the current study, a relationship between all of the stimuli and their relative distance from the design problem is discovered. The results confirm that “near” and “far” are relative terms, and depend on the characteristics of the potential stimuli. Further, although the literature has shown that “far” analogical stimuli are more likely to lead to the generation of innovative solutions with novel characteristics, there is such a thing as too far. That is, if the stimuli are too distant, they then can become harmful to the design process. Importantly, as well, the data mapping approach to identify analogies works, and is able to impact the effectiveness of the design process. This work has implications not only in the area of finding inspirational designs to use for design by analogy processes in practice, but also for synthesis, or perhaps even unification, of future studies in the field of design by analogy.

A Blackboard Architecture to Support Case-Based Reasoning in Mechanical Design

1992 ◽  
Author(s):  
Theodore Bardsz ◽  
Ibrahim Zeid
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Mechanical Design ◽  
Maximum Amount ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Design Plan ◽  
Case Based

Abstract One of the most significant issues in applying case-based reasoning (CBR) to mechanical design is to integrate previously unrelated design plans towards the solution of a new design problem. The total design solution (the design plan structure) can be composed of both retrieved and dynamically generated design plans. The retrieved design plans must be mapped to fit the new design context, and the entire design plan structure must be evaluated. An architecture utilizing opportunistic problem solving in a blackboard environment is used to map and evaluate the design plan structure effectively and successfuly. The architecture has several assets when integrated into a CBR environment. First, the maximum amount of information related to the design is generated before any of the mapping problems are addressed. Second, mapping is preformed as just another action toward the evaluation of the design plan. Lastly, the architecture supports the inclusion of memory elements from the knowledge base in the design plan structure. The architecture is implemented using the GBB system. The architecture is part of a newly developed CBR System called DEJAVU. The paper describes DEJAVU and the architecture. An example is also included to illustrate the use of DEJAVU to solve engineering design problems.

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.

scholarly journals Different Types of Collaborative Problem-Solving Processes in an Online Environment: Solution Oriented Versus Problem Oriented

2017 ◽  
Vol 56 (8) ◽  
pp. 1277-1295 ◽  
Author(s):  
Kyungbin Kwon ◽  
Donggil Song ◽  
Annisa R. Sari ◽  
Umida Khikmatillaeva
Keyword(s):  
Preservice Teachers ◽  
Problem Solving ◽  
Domain Knowledge ◽  
Collaborative Problem Solving ◽  
Learning Context ◽  
Solution Quality ◽  
Online Collaborative Learning ◽  
Problem Solving Process ◽  
Problem Solving Task

The purpose of this study was to investigate the types of problem-solving behaviors and their effects on solution quality in an online collaborative learning context. A total of 12 preservice teachers enrolled in a computer education course participated in the study. Students in pairs, randomly assigned by the instructor, carried out a problem-solving task and then changed partners for subsequent tasks. The problem-solving processes of 25 pairings of students were analyzed. Data on their problem-solving behaviors, the quality of their solutions, and their domain knowledge were collected. Results revealed that students who demonstrated more solution-oriented behaviors led others to better solutions while collaborating. In contrast, students who had difficulty in understanding problems demonstrated more problem-oriented behaviors. The solution-oriented students also gained better domain knowledge at the end, compared with the problem-oriented ones. The effects of the student’s interactions during the problem-solving process were discussed.

scholarly journals Enhancing the Quality of Learning and Understanding of First Year Mathematics for Computer Science Related Majors

10.28945/2635 ◽  
2003 ◽  
Author(s):  
Francis Suraweera
Keyword(s):  
Problem Solving ◽  
Traditional Classroom ◽  
Discrete Mathematics ◽  
First Year ◽  
Classroom Situation ◽  
Common Goal ◽  
First Year Students ◽  
Mental Operations ◽  
Over Time

Most courses on Discrete Mathematics are designed to emphasize problem solving, in general. When the goal is to cover the content, the learning and understanding takes a second place. Over time, the students’ understanding will have large gaps of knowledge that leads to non-enjoyment of the course and a great deal of anxiety. Given the choice, most first year students would not do the Discrete Mathematics course. It is not an easy course to teach because of the opposing expectations of the instructor and students. However, as instructors, we all share a common goal: we would like our students to acquire the skills to perform complex mental operations so that they will be successful in the classroom as well as their future careers. In this paper, we present a way to enhance the learning and understanding of Discrete Mathematics whether it is offered in a distance learning mode or a traditional classroom situation.

scholarly journals Applying Neuroscience to Emergent Processes in Teams

2020 ◽  
pp. 109442812091551
Author(s):  
Danni Wang ◽  
David A. Waldman ◽  
Pierre A. Balthazard ◽  
Maja Stikic ◽  
Nicola M. Pless ◽  
...  
Keyword(s):  
Problem Solving ◽  
Real Time ◽  
Team Dynamics ◽  
Team Members ◽  
The Real ◽  
Research Questions ◽  
Quantitative Electroencephalogram ◽  
Further Development ◽  
Problem Solving Task ◽  
Over Time

In this article, we describe how neuroscience can be used in the study of team dynamics. Specifically, we point out methodological limitations in current team-based research and explain how quantitative electroencephalogram technology can be applied to the study of emergent processes in teams. In so doing, we describe how this technology and related analyses can explain emergent processes in teams through an example of the neural assessment of attention of team members who are engaged in a problem-solving task. Specifically, we demonstrate how the real-time, continuous neural signatures of team members’ attention in a problem-solving context emerges in teams over time. We then consider how further development of this technology might advance our understanding of the emergence of other team-based constructs and research questions.

scholarly journals Semantic Analysis Approach to Studying Design Problem Solving

2019 ◽  
Vol 1 (1) ◽  
pp. 1823-1832
Author(s):  
Georgi V. Georgiev ◽  
Danko D. Georgiev
Keyword(s):  
Problem Solving ◽  
Design Problem ◽  
Semantic Analysis ◽  
Semantic Networks ◽  
Analysis Approach ◽  
Design Solution ◽  
Lexical Database ◽  
Semantic Approach ◽  
Time Dynamics ◽  
Problem Solving Process

AbstractTo objectively and quantitatively study transcribed protocols of design problem solving conversations, we propose a semantic analysis approach based on dynamic semantic networks of nouns constructed with WordNet 3.1 lexical database. We examined the applicability of the semantic approach focused on a dynamic evaluation of the design problem solving process in educational settings. Using a case of real- world design problem-solving conversations, we show that the approach is able to determine the time dynamics of semantic factors such as level of abstraction, polysemy or information content, and quantify convergence/divergence of semantic similarity in design conversations between students, instructors and real clients. The approach can also be used to evaluate the aforementioned semantic factors for successful and unsuccessful ideas generated in the process of design problem solving, or to assess the effect of external feedback on the developed design solution. The proposed semantic analysis approach allows fast computation of the semantic factors in real time thereby demavonstrating a potential for both monitoring and support of the design problem solving process.

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