- Award ID(s):
- NSF-PAR ID:
- Date Published:
- Journal Name:
- Journal of expertise
- Page Range / eLocation ID:
- 190 - 207
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Anders Ericsson’s seminal research on expert performance spurred a number of streams of research across psychological disciplines. Though his work was primarily focused on expert individual performance, there has been increasing interest over the past several decades on the factors underlying expert teamwork. This paper advances eight principles of expert team performance based on decades of team science research: shared mental models, learning and adaptation, role clarity, shared vision, dynamic leadership, psychological safety, cooperation and coordination, and resilience. In addition, we review a number of team development interventions aimed at building team expertise including team training, simulation, coaching, and debriefing. Accordingly, this paper is divided into three sections addressing (1) how expert teams perform, (2) interventions to develop expert team performance, and (3) a reflection on the role Anders Ericsson’s work has played in team science, including a personal reflection from Eduardo Salas on deliberate and guided practice.more » « less
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In a 3‐year design‐based research study, we developed a novel approach to teaching students to take on real‐world engineering design projects with real clients, users, and contexts to engage in expert iterative practices.
Study 1 confirms that student teams struggle to engage in expert iterative practices, even when supported by problem‐based learning (PBL) coaching. Study 2 tests our novel approach, Planning‐to‐Iterate, which uses (i) templates, (ii) guiding questions to help students to define problem and solution elements, and (iii) risk checklists to help student teams to identify risks. We found that student teams using Planning‐to‐Iterate engaged in more expert iterative practices while receiving less PBL coaching.
This work empirically tests a design argument—a theory for a novel teaching approach—that augments PBL coaching and helps students to identify risks and engage in expert iterative practices in engineering design projects.
We often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice (PWEA) framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well.
Many scholars agree that both expert and lay knowledge are needed to gain a fuller understanding of environmental problems, both to find answers to the problems and to improve relations between experts and laypeople. When experts ignore lay knowledge, laypeople can resist by accusing experts of arrogance or conspiracy. Rural people who live among large carnivores like wolves and grizzly bears sometimes distrust expert knowledge or even promulgate conspiracy theories. One's knowledge is inextricably linked with one's identity and social relationships. In this ethnographic study, I examine how a Montana‐based non‐profit, Blackfoot Challenge (BC), facilitates the exchange of knowledge between experts and laypeople for carnivore management. Nurturing expert‐lay relationships is one strategy that BC uses in concert with two others to bridge the expert‐lay knowledge divide: facilitating learning experiences and relying on intermediaries. Knowledge exchanged within expert relationships allows experts to better understand the needs of laypeople and adapt their work to meet those needs while also disseminating expert knowledge to laypeople in a way that earns their trust. The trust built within expert‐lay relationships facilitates the exchange of knowledge, but the way experts and laypeople exchange knowledge also builds trust.