Toward a Taxonomy Linking Game Attributes to Learning

Wendy L. Bedwell, Davin Pavlas, Kyle Heyne, Elizabeth H. Lazzara and Eduardo Salas

Simulation & Gaming, 2012, vol. 43, issue 6, 729-760

Abstract: 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.

Keywords: card sort; computer-based training; game attribute; game attribute taxonomy; learning; learning outcomes; mental model; serious games; simulation/gaming; subject matter experts; taxonomy (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:sae:simgam:v:43:y:2012:i:6:p:729-760

DOI: 10.1177/1046878112439444

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