Toward a Taxonomy Linking Game Attributes to Learning: An Empirical Study

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageAmerican English
JournalSimulation & Gaming
Volume43
DOIs
StatePublished - Dec 1 2012
Externally publishedYes

Keywords

  • card sort
  • computer-based training
  • game attribute
  • game attribute taxonomy
  • learning
  • learning outcomes
  • mental model
  • serious games
  • simulation/gaming
  • subject matter experts
  • taxonomy

Disciplines

  • Educational Methods
  • Human Factors Psychology
  • Other Education
  • Scholarship of Teaching and Learning

Cite this