Kyle Heyne

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

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.

Game-based Learning: The Impact of Flow State and Videogame Self-efficacy

The science of serious games is growing at a substantial pace, providing new insights into the nature of game-based learning. Recently, research has begun to focus on the elements that comprise serious games and how these elements relate to learning (Wilson et al., 2009; Pavlas et al., 2009). As part of an effort to understand how these attributes impact learning outcomes, a study manipulating a number of game attributes in an immune system game was conducted. From this effort, two psychological constructs initially considered as mere covariates – video game self-efficacy and flow state – emerged as significant and highly explanatory predictors of learning. This article provides an overview of these constructs, describes the research that led to this finding, presents the results of this research, and offers implications and suggestions for future work.

An Investigation on the Effects of Flow State on Team Process and Outcomes

There has been an abundance of research on flow state at the individual level and it is often applied to experiences that are typically intended to be enjoyable (e.g., video games and sports). Research has shown that flow state can also be experienced in traditional work environments and several antecedents to its achievement in such environments have been identified. Despite this, there remains some ambiguity regarding the applicability of flow state to teams. Additionally, the majority of the research regarding the experience of flow state in teams revolves around athletic teams. In this paper an argument is presented towards the view that the effect of flow state on team performance is similar to the effect of flow state on individuals but with an additional impact on team processes. The experiment conducted yielded results suggesting a linkage between team flow state and team processes and performance for a complex planning task.

Investigating the Attributes in Serious Games that Contribute to Learning

Though serious games have been the topic of much discussion within the scientific community, the theoretical underpinnings of this technology remains largely nascent. Although much of the knowledge base of the simulation and digital learning community applies to serious games, the play-based nature of these tools necessitates additional examination. Games have defining component attributes, and variation of these attributes may affect learning resulting from game play. Thus, understanding of the attributes involved in games and how they map to various learning outcomes will improve the communitys ability to effectively design and employ these tools. This article describes research designed to address this need through experimental manipulations of the privately developed InnerCell game. This effort is conducted in the context of game attributes and learning outcomes, and recommendations for attribute-outcome pairs are offered based on preliminary findings of this research.

Guidelines for Training Adaptive Expertise

People operating in todays society frequently encounter complex, dynamic, and ambiguous situations; thus, people must possess mental agility and the ability to adjust quickly yet accurately. Therefore, there is a need to train adaptive expertise to performance successfully. To that end, the purpose of this paper is to provide researchers and practitioners with a review of the literature pertinent to developing training targeting adaptive expertise. This review is presented in the form of guidelines for designing and implementing training interventions specifically focusing on adaptive expertise.

Utilizing telemedicine in the trauma intensive care unit: does it impact teamwork?

BACKGROUND The aim of this study was to examine the impact of a telemedical robot on trauma intensive care unit (TICU) clinician teamwork (i.e., team attitudes, behaviors, and cognitions) during patient rounds. MATERIALS AND METHODS Thirty-two healthcare providers who conduct rounds volunteered to take surveys assessing teamwork attitudes and cognitions at three time periods: (1) the onset of the study, (2) the end of the 30-day control period, and (3) the end of the 30-day experimental period, which immediately followed the control period. Rounds were recorded throughout the 30-day control period and 30-day experimental period to observe provider behaviors. For the initial 30 days, there was no access to telemedicine. For the final 30 days, the rounding healthcare providers had access to the RP-7 robot (Intouch Health Inc., Santa Barbara, CA), a telemedical tool that can facilitate patient rounds conducted away from bedside. RESULTS Using a one-tailed, one-way repeated-measures analysis of variance (ANOVA) to compare trust at Times 1, 2, and 3, there was no significant effect on trust: F(2, 14)=1.20, p=0.16. When a one-tailed, one-way repeated-measures ANOVA to compare transactive memory systems (TMS) at Times 1, 2, and 3 was conducted, there was no significant effect on TMS: F(2, 15)=1.33, p=0.15. We conducted a one-tailed, one-way repeated-measures ANOVA to compare team psychological safety at Times 1, 2, and 3, and there was no significant effect on team psychological safety: F(2,15)=1.53, p=0.12. There was a significant difference in communication between rounds with and without telemedicine [t(25)=-1.76, p<0.05], such that there was more task-based communication during telerounds. Telemedicine increased task-based communication and did not negatively impact team trust, psychological safety, or TMS during rounds. CONCLUSIONS Telemedicine may offer advantages for some teamwork competencies without sacrificing the efficacy of others and may be adopted by intact rounding teams without hindering teamwork.

Best Practices for the Effective Implementation of Telerounding

The research base for telemedicine is expanding with nearly the voracity that the implementation of tele-medicine systems has. Telerounding is one specific subset of telemedicine where a team of physicians will gather in a specific location and use a telepresence robot to perform their day to day rounding procedures. This type of telemedicine is fairly new, and thus is lacking a solid research base to guide the implementation and usage of such a system. This paper is an initial attempt at a comprehensive list of best practices for such a system and is based off of current telemedicine literature as well as the experience of the authors.

On the performance of fitness uniform selection for non-deceptive problems

Genetic algorithms (GAs) are probabilistic search techniques inspired by natural evolution. Selection schemes are used by GAs to choose individuals from a population to breed the next generation. Proportionate, ranking and tournament selection are standard selection schemes. They focus on choosing individuals with high fitness values. Fitness Uniform Selection Scheme (FUSS) is a recently proposed selection scheme that focuses on fitness diversity. FUSS have shown better performance than standard selection schemes for deceptive and NP-complete problems. In general, it is difficult to determine whether a real-life problem is deceptive or not. However, there is no information about the relative performance of FUSS on non-deceptive problems. In this paper, the standard selection schemes mentioned above were compared to FUSS on two non-deceptive problems. A GA using FUSS was able to find high-fitness solutions faster than expected. Consequently, FUSS could be a good first-choice selection scheme regardless of whether a problem at hand is deceptive or not.