The Impact of Teaming and Cognitive Style on Student Perceptions of Design Ideation Outcomes

Jablokow, Kathryn; Teerlink, Wesley; Yılmaz, Seval; Daly, Shanna R.; Silk, Eli M.

doi:10.18260/p.24885

Cited by 12 publications

(20 citation statements)

References 27 publications

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“…Numerous techniques developed over the last half century help facilitate concept generation (Helm et al 2016;Jablokow et al 2015;Bohm et al 2005). These techniques can be broadly classified as either structured or unstructured (Gero et al 2013;Moon Sungwoo et al 2012;Shah et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Patterns of Cortical Activation When Using Concept Generation Techniques of Brainstorming, Morphological Analysis, and TRIZ

Shealy

Gero

2018

Volume 7: 30th International Conference on Design Theory and Methodology

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This paper presents the results of an experimental study comparing cortical activation in the brain when generating solutions using brainstorming, morphological analysis, and TRIZ. Twelve engineering students were given the same three design tasks, respectively, using the three solution generation techniques. Students generated solutions while change in oxygenated blood along the prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy. The results show that generating solutions using brainstorming, morphological analysis, and TRIZ leads to differences in cortical activation, specifically along the region of the brain associated with spatial working memory, cognitive flexibility, and abstract reasoning, called the left dorsolateral prefrontal cortex (left DLPFC). Brainstorming evokes a high average blood oxygenation level dependent (BOLD) response in the left DLPFC early during the solution generation process but this high response is not sustained. In comparison, morphological analysis and TRIZ evoke multiple high average BOLD responses across the solution generation process. Not only was the high average BOLD response sustained but the density of network coordination among brain regions across the PFC was greater for morphological analysis and TRIZ. Higher density is a proxy for higher cognitive effort. The brain regions most central to coordination also varied. During brainstorming the right hemisphere, in a region associated with memory encoding (right PFC), was most activated. During morphological analysis, the left hemisphere, the left DLPFC was most activated. During TRIZ, both the middle and left hemisphere included regions of high activation. These results indicate neuro-cognitive differences of activation patterns, cognitive effort over time, and brain regions central for coordination when using these three concept generation techniques. Future research can begin to explore neuro-cognitive differences as a result of these techniques over multiple uses and the effects of design education.

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Section: Introductionmentioning

confidence: 99%

Patterns of Cortical Activation When Using Concept Generation Techniques of Brainstorming, Morphological Analysis, and TRIZ

Shealy

Gero

2018

Volume 7: 30th International Conference on Design Theory and Methodology

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“…Their results suggest that as the cognitive gap between team members increases, the more adaptive team member tends to feel that they contributed less to team ideation, while the more innovative team member tends to feel that they contributed more. In other previous work by Jablokow et al [21], the results suggest that the presence of more innovative individuals on a team may be correlated with a greater occurrence of unique ideas in the team, where unique was defined as new to the current team discussion. They also found that more innovative teams (by KAI mean) tended to exhibit team interactions with a higher degree of integration between topics.…”

Section: Cognitive Style Diversity and Design Teamsmentioning

confidence: 51%

“…In contrast, more innovative individuals prefer less structure, with less concern about consensus, and tend to make radical changes that may ignore rules in an attempt to make the current system work "differently"; their efforts may or may not lead to improvements in that system. Kirton's A-I cognitive style influences both individual problem-solving characteristics and team interactions, and its effects have been studied in observations of engineering teams [22,24,25,21]. However, it is unclear how KAI style could be used to inform the formation of engineering teams.…”

Section: List Of Symbolsmentioning

confidence: 99%

“…were homogeneous), overall team success increased as a result of enhanced inter-team communication, while diverse (i.e., heterogeneous) teams were more likely to fail as a result of unresolved cognitive gaps. Jablokow et al [21] explored the effects of cognitive gaps on dyad performance and interactions between design team members during concept generation. Their results suggest that as the cognitive gap between team members increases, the more adaptive team member tends to feel that they contributed less to team ideation, while the more innovative team member tends to feel that they contributed more.…”

Section: Cognitive Style Diversity and Design Teamsmentioning

confidence: 99%

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Designing Like a Human: Simulating Cognitive Style in Teamwork with an Agent-Based Model

Lapp¹

2019

Preprint

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This thesis describes the development of an agent-based model for simulating cognitive style in the context of collaborative problem solving. Cognitive style describes the diverse ways in which people solve problems. Individuals’ cognitive styles can impact the success or failure of a design team. However, the effects of cognitive style in collaborative problem solving are not well understood. To address this gap, this thesis presents KABOOM (KAI Agent-Based Organizational Optimization Model), the first agent-based model of teamwork to incorporate cognitive style. In this thesis, experiments using KABOOM investigate the interacting effects of a design team’s communication patterns, specialization, and cognitive style composition on a team’s performance. Testing the model with a race car design problem reveals that teams can strategically leverage diversity of cognitive style to improve performance. By simulating cognitive style and team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.

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“…However, this information doesn't provide a robust way to estimate characteristics of the participants that might lead to interpersonal issues or synergistic team combinations. Specific psychometrics that are impactful in teams and may be of use for building a more nuanced model of team performance here include: personality, using a five-factor model for instance (Salleh, Mendes, Grundy, & Burch, 2009, 2010; cognitive style, a measure of an individual's preferred approach in solving problems (Buffinton, Jablokow, & Martin, 2002;Jablokow, Teerlink, Yilmaz, Daly, & Silk, 2015); and cognitive level, commonly referred to as expertise or knowledge (Ehrlich & Chang, 2006;Faraj & Sproull, 2000).…”

Section: Limitationsmentioning

confidence: 99%

An analysis of design process and performance in distributed data science teams

Maier

DeFranco

McComb

2019

TPM

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PurposeOften, 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.Design/methodology/approachWe 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.FindingsThis work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.Research limitations/implicationsThese results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.Originality/valueThese results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

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The Impact of Teaming and Cognitive Style on Student Perceptions of Design Ideation Outcomes

Cited by 12 publications

References 27 publications

Patterns of Cortical Activation When Using Concept Generation Techniques of Brainstorming, Morphological Analysis, and TRIZ

Patterns of Cortical Activation When Using Concept Generation Techniques of Brainstorming, Morphological Analysis, and TRIZ

Designing Like a Human: Simulating Cognitive Style in Teamwork with an Agent-Based Model

An analysis of design process and performance in distributed data science teams

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