Templates for selecting PC-based synthetic environments for application to human performance enhancement and training

Morris, Christina S.; Tarr, Ronald W.

doi:10.1109/vr.2002.996513

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…In each of these cases, the data provided were not useful to the goal of Empirical 42. Morris & Tarr, 2002 collapsing game attributes into categories. As this was a result of participants not reading or failing to understand the card sort directions, the data were deleted and the participant information removed from the demographic information, resulting in a total of six subjects removed.…”

Section: Limitationsmentioning

confidence: 99%

Toward a Taxonomy Linking Game Attributes to Learning

Bedwell

Pavlas

Heyne

et al. 2012

Simulation & Gaming

328

314

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The serious games community is moving toward research focusing on direct comparisons between learning outcomes of serious games and those of more traditional training methods. Such comparisons are difficult, however, due to the lack of a consistent taxonomy of game attributes for serious games. Without a clear understanding of what truly constitutes a game, scientific inquiry will continue to reveal inconsistent findings, making it hard to provide practitioners with guidance as to the most important attribute(s) for desired training outcomes. This article presents a game attribute taxonomy derived from a comprehensive literature review and subsequent card sorts performed by subject matter experts (SMEs). The categories of serious game attributes that emerged represent the shared mental models of game SMEs and serve to provide a comprehensive collection of game attributes. In order to guide future serious games research, the existing literature base is organized around the framework of this taxonomy.

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Section: Limitationsmentioning

confidence: 99%

Toward a Taxonomy Linking Game Attributes to Learning

Bedwell

Pavlas

Heyne

et al. 2012

Simulation & Gaming

328

314

View full text Add to dashboard Cite

show abstract

“…High fidelity graphics and audio can be expensive to produce, and it appears that there are diminishing educational returns from high fidelity systems (Baum, Riedel, Hays, & Mirabella, 1982;Freda & Ozkaptan, 1980;Morris & Tarr, 2002). For example, Morris and Tarr (2002) found that quality of decision-making actually decreased with higher levels of graphics. This suggests that while realism is a feature that may motivate players, there is a limit where greater levels of realism may no longer increase the effectiveness of a training game.…”

Section: Motivation Featuresmentioning

confidence: 99%

Instructional Characteristics and Motivational Features of a PC-based Game

2004

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“…Despite the lack of a definitive taxonomy linking training technologies to learning goals, there are bootstrap efforts underway to fill this vacuum. These efforts focus on the sensory stimulation requirements that these tools must satisfy (Morris & Tarr, 2002). A recurring theme, initially suggested by Roscoe (1982) is that different types of skills have vastly different technical requirements supporting their training.…”

Section: Team Training: Defining the Challengementioning

confidence: 99%

Assessing and Enhancing Team Training Technologies: From the Lab to the Field

Cohn

Stripling

Schmorrow

et al. 2005

Proceedings of the Human Factors and Ergonomics Society Annual

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The primary goal of any training system is to enhance performance on the real world tasks it simulates (Lathan et al., 2002). The principal benefit that Virtual Environment (VE) systems have over field exercises and similar real world training is that VEs present training situations that would be too hazardous or too costly to reproduce in the real world (Rose, Attree, Brooks, Parslow, Penn & Ambihaipahan, 2000). As compared with Legacy type systems or physical mockups VEs also afford a smaller footprint and greater reconfigurability to support a variety of training tasks (Cohn, Helmick, Meyers & Burns, 2000). An additional, relatively unexplored benefit of VE, is the potential ease with which they can be implemented to train team tasks. This panel will investigate the utility of using VE for team training, addressing both theoretical and practical concerns.

show abstract

Templates for selecting PC-based synthetic environments for application to human performance enhancement and training

Cited by 4 publications

References 13 publications

Toward a Taxonomy Linking Game Attributes to Learning

Toward a Taxonomy Linking Game Attributes to Learning

Instructional Characteristics and Motivational Features of a PC-based Game

Assessing and Enhancing Team Training Technologies: From the Lab to the Field

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