Abstract
In this chapter, the author presents a theoretical framework to better understand how individuals adapt to innovative technology used to support learning opportunities. This chapter begins with an overview of literature concerning technology acceptance, specifically centered on the seminal work of Venkatesh, Morris, Davis, and Davis (2003). This is followed by a discussion of three theoretical perspectives change that include Lewin's (1997, 1952) change model, Schein's (1996) adaptation of Lewin's model as it related to learning, and Roger's (2003) diffusion of innovation. Next, the chapter includes a discussion on the concept of digital personalities as framed by Prensky (2001b) and Palfrey and Gasser (2008) and how this concept may impact technology acceptance. The chapter concludes with a discussion of how these theoretical perspectives inform our understanding of technology acceptance.
TopIntroduction
Learning has the potential to be a positive, transformational experience (Cranton, 2002 ; Mezirow, 1991). Yet, the potential also exists that the learning experience will be anxiety-filled and frustrating (Schein, 1996). Learning experiences leave lasting impressions that sweeten or sour an individual’s outlook on future learning opportunities. Creating and delivering meaningful and positive learning opportunities require a careful, thoughtful, and deliberate approach, regardless of how the course is delivered. This approach should enlist a team of professionals that includes instructional designers, technical writers, subject matter experts, managers, and instructors. Quite often, learning opportunities have innovative technology integrated into the course design to engage (McGurn & Prevou, 2012; Pavera, Walkera, & Hunga, 2014) and even entertain (Junginger, 2008) the learners. One example is the use of virtual reality simulations to train police officers and firefighters how to properly respond to high-risk situations. While these simulations may appear to be full size, interactive video games, they provide learners with the experience of the stress and kinesthetic activities without the risk of serious or grave injury. Another example is the use of simulated disaster response props to teach master of business administration (MBA) students team building and decision-making skills (Wilkins, 2014).
Adding innovative technology into learning opportunities also increases the complexity and creates additional challenges and anxieties for learners and instructors alike. These challenges and anxieties can result in decreased performance and negative attitudes about the learning opportunity. This is often the result of learners rejecting an innovative technology. The risk posed by these challenges may discourage some learning developers from integrating innovative technologies into a learning opportunity. However, Rossett and Marshall (2010) suggest that failing to integrate innovative technology into learning applications results in “opportunities … being left on the table” (p. 7).
The purpose of this chapter is to examine a framework that may help instructional designers, instructors, and other learning professionals understand the issue of technology acceptance, especially the acceptance of innovative technologies used in learning applications. To accomplish this, I present a framework that explains how individuals adapt to innovative technology used to support learning. As Figure 1 illustrates, I weave together the theoretical perspectives of technology acceptance (Davis, 1986; Venkatesh et al., 2003), change specific to learning (Lewin, 1997, 1952; Schein, 1996); Rogers’s (2003) diffusion of innovation and the discussion of digital personalities (Palfrey & Gasser, 2008; Prensky, 2001a). In this chapter, I will provide a unique perspective on the use of innovative technology in learning opportunities that informs scholars and practitioners alike. I conclude with a discussion of the implications drawn from these perspectives and how they can enhance the design of learning experiences through improving the understanding of technology acceptance.
Key Terms in this Chapter
Innovative Technology: Technology that is innovative or used in an innovative method.
Simulation: “A production of visual images of objectives and scenes, usually under real-time conditions, when the original object or scene is not available” ( Welford, 1977 , p. 784).
Digital Personality: An individual’s attitudes, interests, social roles, and other traits that relate to his or her use of technology.
Learning: “A process of constructing meaning; how people make sense of their experience” ( Merriam & Caffarella, 1999 , p. 261).
Innovation: “[A]n idea, practice, or object that is perceived as new by an individual or other unit of adoption” ( Rogers, 2003 , p. 474). The newness of an innovation is relative. It does not have to be a completely new knowledge. Innovation can include knowledge that is forgotten or known for some time. As Rogers (2003) states, “If an idea is new to the individual, it is an innovation” (p. 12).
Digital Settler: Located on the digital personality spectrum between digital natives and digital immigrants. While these people use technology, they still rely heavily on other analog forms of interaction. For example, they may use a web-based service pay their cable bill, but will use a hardcopy phone book to look up the number for the cable company to report an outage ( Palfrey & Gasser, 2008 ).
Digital Native: A person born into the digital age (after 1980) who has access to networked digital technologies and strong computer skills and knowledge. Digital natives share a common global culture that is defined not strictly by age but by certain attributes and experiences related to how they interact with information technologies, information itself, one another, and other people and institutions ( Palfrey & Gasser, 2008 , p. 346).
Digital Immigrant: “[A] person who has adopted the internet and related technologies, but who was born prior to the advent of the digital age” ( Palfrey & Gasser, 2008 , p. 346).