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Simulations have been used to model complex environments in numerous domains, such as medical (e.g., Nestel, Groom, Eikeland-Husebø, & O'Donnell, 2011; Steadman et al., 2006), military (e.g., Hill et al., 2006; King et al., 2006; Raybourn, Deagle, Mendini, & Heneghan, 2005), emergency response (e.g., Jenvald & Morin, 2004; Reznek et al., 2003), and corporate industries (e.g., Forssén & Haho, 2001; Jana, 2006; Summers, 2004). With this widespread adoption, definitions and labels have often been blurred across researchers, as simulations can be referred to as games, computer-based simulations, or more recently serious games (Schollmeyer, 2006). Research on the effectiveness of simulations from multiple disciplines has enhanced the generalizability of the findings on their effectiveness, but has limited the development of a shared framework and understanding on what simulations are and what makes them effective.
This problem is further compounded by the fact that simulations are typically categorized based on the purpose of play as opposed to the learning components of play, an issue which recent reviews of the literature have tried to address. In particular, Sitzmann (2011) conducted a meta-analysis on 65 independent studies across several domains and noted that the lack of a shared definition of simulations and games was a major barrier in evaluating their effectiveness. In an attempt to consolidate the various definitions of simulations, the author defined computer-based simulation games as “instruction delivered via personal computer that immerses trainees in a decision-making exercise in an artificial environment in order to learn the consequences of their decisions” (p. 492). Another conceptual development in the area of simulations includes work by Wilson and colleagues (2009) who proposed a framework on the attributes of simulations. Wilson et al. (2009) defined eighteen simulation attributes, such as fantasy, representation, sensory stimuli, challenge, mystery assessment, and control. They also offered propositions on how these attributes linked to key cognitive, skill-based, and affective learning outcomes in players. Despite the thoroughness of their review, little empirical research has been conducted in this area.
In an attempt to address some of the propositions described in Wilson and colleagues’ (2009) framework, the purpose of the current study is to operationalize a key simulation attribute in order to understand its impact on learning. More specifically, this study will examine how fantasy impacts affective and skill-based learning outcomes. Fantasy is a very common attribute found in many commercial video games and simulations (Wilson et al., 2009). It has been implemented in early computer games such as SID MIERS CIVILIZATION (MicroProse, 1991), to more modern games such as WORLD OF WARCRAFT (Blizzard, 2004). Evoking fantasy has also been found to be one of the most useful ways to engage and motivate players in a game (Lortz, 1979; Malone, 1980; Myers, 1990).