Implicit Learning, Tacit Knowledge, Expertise Development, and Naturalistic Decision Making

Patterson, Robert; Pierce, Byron J.; Bell, Herbert H.; Klein, Gary

doi:10.1177/155534341000400403

Cited by 42 publications

(26 citation statements)

References 65 publications

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“…In contrast to Benner's model which featured ‘knowledge’ as the lynchpin to expertise, our findings suggest that experience is the key to expert status for clinicians in the PED. In relation to ‘experience’, previous work has suggested that intuitive decision making can be achieved by individuals through a process of implicit learning that results in ‘pattern recognition’ 21. The results from our study conform to these findings and also define how clinicians in the PED apply analytical decision making, a feature stronger in trainees who rely on evidence-based guidelines and support from colleagues while experience is being gained.…”

Section: Discussionsupporting

confidence: 86%

The transition to clinical expert: enhanced decision making for children aged less than 5 years attending the paediatric ED with acute respiratory conditions

Bowen¹,

Shaw²,

Lyttle

et al. 2016

Emerg Med J

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BackgroundRates of unplanned paediatric admissions are persistently high. Many admissions are short-stay events, lasting less than 48 hours.ObjectiveThis qualitative research explores factors that influence clinical decision making in the paediatric ED (PED) for children under 5 attending with acute respiratory conditions, focusing on how management decisions adapt with increasing experience.MethodSemi-structured interviews were conducted with 15 PED clinicians (doctors, emergency nurse practitioners and registered nurses) with varying levels of experience in paediatric emergency medicine (PEM), emergency medicine or paediatrics. Audio-recorded interviews were transcribed and analysed thematically.ResultsThere were clear differences in decision-making approaches between experienced clinicians and junior staff. The latter were more risk adverse, relying heavily on guidelines, set admission criteria, clinical theory and second opinions. This was particularly true for doctors. ‘Informal’ learning was apparent in accounts from less-experienced doctors and nurses, whereby tacit knowledge and risk management played an increasing role in the development of clinical intuition that permitted rapid assessment and treatment of young patients.ConclusionsThe emergence of intuition entwined with approaches to risk management and the role of these skills in clinical decision making, carry implications for the development of training programmes for clinicians working in PEM. Enhanced training for such groups to permit development of the supplementary skills described in this study could have the ability to improve care delivery and even reduce paediatric admissions.

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Section: Discussionsupporting

confidence: 86%

The transition to clinical expert: enhanced decision making for children aged less than 5 years attending the paediatric ED with acute respiratory conditions

Bowen¹,

Shaw²,

Lyttle

et al. 2016

Emerg Med J

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“…The dual-processes are presented in Figure 9, which include pattern recognition and consciousness in working memory going from the stimulus (sensation) to the response (exploitation). Patterson, et al, [38] has used the model for natural decision making that links implicit/tacit (System 1) with explicit (System 2) analysis. Information fusion by a machine has also related explicit (LLIF) with that tacit (HLIF) data processing.…”

Section: Sys1 and Sys 2 Dual Process Modelmentioning

confidence: 99%

Multi-Intelligence Analytics for Next Generation Analysts (MIAGA)

Blasch

Waltz

2016

SPIE Proceedings

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Current analysts are inundated with large volumes of data from which extraction, exploitation, and indexing are required. A future need for next-generation analysts is an appropriate balance between machine analytics from raw data and the ability of the user to interact with information through automation. Many quantitative intelligence tools and techniques have been developed which are examined towards matching analyst opportunities with recent technical trends such as big data, access to information, and visualization. The concepts and techniques summarized are derived from discussions with real analysts, documented trends of technical developments, and methods to engage future analysts with multiintelligence services. For example, qualitative techniques should be matched against physical, cognitive, and contextual quantitative analytics for intelligence reporting. Future trends include enabling knowledge search, collaborative situational sharing, and agile support for empirical decision-making and analytical reasoning.

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“…In Feldon (2007), Feldon stated that an expert is unable to explain his/her superior performance because the amount of expertise inhibits his/her explanation skills. This is because an expert typically has more knowledge than he/she can verbalize (Patterson, Pierce, Bell, & Klein, 2010), which impedes the capability of the expert as a teacher. In addition, an expert is unaware of the factors behind his/her superior performance.…”

Section: Capture Of Expert Performancementioning

confidence: 99%

Supporting Training of Expertise with Wearable Technologies: The WEKIT Reference Framework

Limbu

Fominykh²,

Klemke

et al. 2017

Mobile and Ubiquitous Learning

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Abstract. In this chapter, we present a conceptual reference framework for designing augmented reality applications for supporting training. The framework leverages the capabilities of modern augmented reality and wearable technology for capturing the expert's performance in order to train expertise. It has been designed in the context of WEKIT project which intends to deliver a novel technological platform for industrial training. The framework identifies the state-of-art augmented reality training methods which we term as "transfer mechanism" from an extensive literature review. Transfer mechanisms exploit the educational affordances of augmented reality and wearable technology to capture the expert performance and train the novice. The framework itself is based upon Merrienboer's 4CID model which is suitable for training complex skills. The 4CID model encapsulates major elements of apprenticeship models which is a primary method of training in industries. The framework itself complements the 4CID model with expert performance data captured with help of wearable technology which is then, exploited in the model to provide a novel training approach for efficiently and effectively master the skills required. In this chapter, we will give a brief overview of our current progress in developing this framework. Bibeg limbu:Bibeg limbu is currently a PhD student at Open University of Netherlands. He received his Master in Educational Technology from University of Saarland, Germany. He's a games, multimedia and instructional designer by education and is interested in researching expertise, Augmented Reality and Wearable Technology.Mikhail Fominykh: Mikhail Fominykh is an enthusiast, researcher, developer, and project manager in the field of Technology-Enhanced Learning. He currently is an associate professor at Molde University College (Norway), an adjunct professor at the Volga State University of Technology (Russia), and a project manager at Europlan-UK ltd (United Kingdom). In 2015, he started and coordinated a successful Horizon 2020 grant proposal which is now the WEKIT research project, developing training with augmented reality. Mikhail obtained his PhD at the Department of Computer and Information Science at the Norwegian University of Science and Technology, and later did a postdoc at the Program for Learning with ICT at the same university. He has experience in the area of technology-enhanced learning from working on several national and international R&D projects, developing successful grant proposals, developing educational simulators, courses, training programs, and their technological support. He published in several journals and books, and presented research findings at over 50 academic conferences. Roland Klemke: Prof. Dr. Roland Klemke is researcher at the Welten Institute of the Open University of the Netherlands. He leads national and international research projects in the TEL field. Research topics include serious gaming, mobile learning, augmented reality, open architectures, emerging standards, we...

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Implicit Learning, Tacit Knowledge, Expertise Development, and Naturalistic Decision Making

Cited by 42 publications

References 65 publications

The transition to clinical expert: enhanced decision making for children aged less than 5 years attending the paediatric ED with acute respiratory conditions

The transition to clinical expert: enhanced decision making for children aged less than 5 years attending the paediatric ED with acute respiratory conditions

Multi-Intelligence Analytics for Next Generation Analysts (MIAGA)

Supporting Training of Expertise with Wearable Technologies: The WEKIT Reference Framework

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