Comparing Design Outcomes Achieved by Teams of Expert and Novice Designers Through Agent-Based Simulation

Singh, Harshika; Cascini, Gaetano; McComb, Christopher

doi:10.1017/pds.2021.66

Cited by 4 publications

(4 citation statements)

References 25 publications

(24 reference statements)

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“…Compared to students, practitioners may spend more time problem-scoping and information gathering (Atman et al, 2007), create more extensive mental representations of the problem (Björklund, 2016), and use more design strategies and rely less on trial-and-error (Ahmed et al, 2003). However, they produce a lower variety of solutions (Cash, Hicks, and Culley, 2012;Singh, Cascini, and McComb, 2021). Little is known about how practitioner tutors differ from academic tutors.…”

Section: Practitioner Tutors In the Design Studiomentioning

confidence: 99%

COMPARING ACADEMICS AND PRACTITIONERS Q & A TUTORING IN THE ENGINEERING DESIGN STUDIO

Hurst,

Lin,

Treacy

et al. 2023

Proc. Des. Soc.

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In the design studio, academic (professor) and practitioner tutors provide individual mentoring to students as they progress in their design projects. Prior studies suggest that design practitioners may follow a different design process compared to academics, but little is known about how this difference relates to their design tutoring. This study explores the similarities and differences in tutoring by academics and practitioners. We use a question-asking lens to characterize the tutoring styles of four tutors - two academics and two practitioners - over a five-week design project in an engineering design studio. We find that academic tutors ask questions at a significantly higher rate than practitioner tutors, suggesting a more question-centred tutoring style. We also find that proportionally more of practitioner tutors’ questions are generative in nature, while the academic tutors employ more convergent thinking in their questioning. This may be an indicator of the practitioners' own design thinking, which might be more solution-focused than that of academics. These preliminary findings motivate future investigations of the relationship between differences in tutoring and impact on student design learning.

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Section: Practitioner Tutors In the Design Studiomentioning

confidence: 99%

COMPARING ACADEMICS AND PRACTITIONERS Q & A TUTORING IN THE ENGINEERING DESIGN STUDIO

Hurst,

Lin,

Treacy

et al. 2023

Proc. Des. Soc.

View full text Add to dashboard Cite

show abstract

“…Novices, on the other hand, use trial-anderror techniques to solve the current problem [35]. This may cause novices to take more time to reach a satisfactory solution, as shown in prior work [36]. Designer agents who have worked on a design task were stored in a pool as experienced agents.…”

Section: Experience In Designer Agentsmentioning

confidence: 99%

“…Designer agents who have worked on a design task were stored in a pool as experienced agents. Then a few agents from this experienced agent pool and some newly created agents were placed together in a team to work on a design task (more details on the representation of experience in MILANO are provided in [36]).…”

Section: Experience In Designer Agentsmentioning

confidence: 99%

“…Like any other agent-based model that aims to mimic human activities, MILANO also had some assumptions for example the most confident and trustful individuals were considered influencers [32], the agreement in teams is affected by the influencers [33] and experienced agents know the design space based on their past experience [36].…”

Section: Experience In Designer Agentsmentioning

confidence: 99%

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Virtual and face-to-face team collaboration comparison through an agent-based simulation

Singh

Cascini²,

McComb

2022

Journal of Mechanical Design

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The ongoing COVID-19 pandemic has accelerated the acceptance of virtual team collaboration as a replacement for face-to-face collaboration. Unlike face-to-face collaboration, virtual collaboration is influenced by unique factors, such as technology mediation. However, there is a lack of rigorous research that assesses the impact of virtual collaboration on the engineering design process. Therefore, the current study investigates the effect of virtual team collaboration on design outcomes by means of the MILANO (Model of Influence, Learning, and Norms in Organizations) framework. To tailor MILANO for virtual collaboration, this paper first presents an empirical study of human design teams, which shows how model parameter values for face-to-face collaboration (like self-efficacy, perceived influencers, perceived degree of influence, trust and familiarity) differ from appropriate parameter values for virtual collaboration. The simulation results for both virtual and face-to-face collaboration show how design outcomes differ with collaboration mode. Unlike teams with a few well-defined influential individuals, the mode of collaboration does not have a significant impact on teams where all individuals are equally influential. Virtual collaboration also results in lower exploration and variety than face-to-face collaboration.

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A Computational Model of Human Proficiency in Engineering Configuration Design

Brownell

Cagan

Kotovsky

2023

Journal of Mechanical Design

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This work introduces the Proficient Simulated Annealing Design Agent Model (PSADA), a cognitively inspired, agent-based model of engineering configuration design. PSADA models different proficiency agents using move selection heuristics and problem space search strategies, both of which are identified and extracted from prior human subject studies. The model is validated with two design problems. Agents are compared to human designers and show the accurate simulation of the behaviors of the different proficiency designers. These behavior differences lead to significantly different performance levels, matching the human performance levels with just one exception. These validated heterogeneous agents are placed into teams and confirmed previous findings that the most proficient member of a configuration design team has the largest impact (positive or negative) on team performance. The PSADA model is introduced as a scalable platform to further explore configuration design proficiency's role in design team performance and organizational behavior.

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Comparing Design Outcomes Achieved by Teams of Expert and Novice Designers Through Agent-Based Simulation

Cited by 4 publications

References 25 publications

COMPARING ACADEMICS AND PRACTITIONERS Q & A TUTORING IN THE ENGINEERING DESIGN STUDIO

COMPARING ACADEMICS AND PRACTITIONERS Q & A TUTORING IN THE ENGINEERING DESIGN STUDIO

Virtual and face-to-face team collaboration comparison through an agent-based simulation

A Computational Model of Human Proficiency in Engineering Configuration Design

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