Expertise Development for Next-Generation Digital Learners

Expertise Development for Next-Generation Digital Learners

Eulho Jung (Boise State University, USA), Doo Hun Lim (University of Oklahoma, USA) and Soo Jeoung Han (Boise State University, USA)
DOI: 10.4018/978-1-5225-3873-8.ch003


As the nature of work has become much more complex and sociotechnical, the needs for developing expertise are high. Research on expertise development are diverse (Bjork, 1994; Einstein & McDaniel, 2005; Schneider et al., 2002), but empirical studies intended to bridge expertise development and instructional design theories are relatively scarce (Ertmer et al., 2008; Fadde, 2009). This chapter addresses why and how scholars and practitioners should foster expertise development skills for Next-Generation digital learners.
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Expertise Studies Overview

Developing expertise takes a substantial amount of time. Ericsson (2009) stated it could take up to 10 years of experience and deliberate practice to declare having expertise. However, schools as well as non-profit and for-profit organizations have always been interested in advancing training methods that help learners/trainers learn faster and more efficiently. To this end, researchers and practitioners have begun developing methods to help non-experts perform like experts by developing theories and models to identify what to teach. Some of the tools and methods that are widely recognized in the field include Macrocognition (Patterson & Miller, 2012; Schraagen et al., 2000), Human-Centered Computing (Hoffman & Militello, 2012), Naturalistic Decision Making (Zsambok & Klein, 2014), Cognitive Systems Engineering (Hollnagel & Woods, 2005), Cognitive Task Analysis (Clark & Estes, 1996; Crandall, Klein, & Hoffman, 2006; Feltovich et al., 1997; Hollnagel, 2003; Jenkins, 2009; Lukas & Albert, 1993), and the field of Expertise Studies in general (Ericsson, 2009; Ericsson et al., 2006; Hoffman, 2007). In order to synthesis these efforts, Hoffman et al. (2013) proposed a concept called Accelerated Expertise, that suggested ways by which to accelerate the learning process to make a non-expert appear to be an expert.

Key Terms in this Chapter

Deliberate Practice: Deliberate practice is a highly structured activity engaged in with the specific goal of improving performance.

Pattern Recognition: Pattern recognition is the process of classifying input data into objects or classes based on key features.

Cognitive Task Analysis: Cognitive task analysis (CTA) is a type of Task analysis aimed at understanding tasks that require a lot of cognitive activity from the user, such as decision-making, problem-solving, memory, attention and judgement.

Metacognition: Metacognition is thinking about one's thinking.

Situational Awareness: Situational Awareness is the ability to identify, process, and comprehend the critical elements of information about what is happening to the team with regards to the mission.

Expertise: Basis of credibility of a person who is perceived to be knowledgeable in an area or topic due to his or her study, training, or experience in the subject matter.

Novice: Literally, someone who is new—a probationary member who has had some (“minimal”) exposure to the domain.

Shared Leadership: Shared leadership is a leadership style that broadly distributes leadership responsibility, such that people within a team and organization lead each other.

Model Domain Learning (MDL): The Model of Domain Learning (MDL) is an alternative perspective on expertise that arose from studies of student learning in academic domains, such as reading, history, physics, and biology.

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