Abstract
Serious games have potential for achieving a variety of effectiveness goals for different stakeholders in complex domains like healthcare. The authors propose a Serious Game Framework (SGF) that provides a conceptual architecture that considers the design alternatives, support for multiple game types on the same architecture, and the ability to assess, research, and improve the learning process. The authors show with examples how with this, on one hand, the same knowledge content can be used in different serious games to achieve different learning outcomes and goals. On the other hand, the same goal can be achieved by different serious games, and user preferences might determine what games are used. The game interaction data also becomes shareable and useful for analysis and continuous improvement.
Top1 Introduction
Serious Games1 are emerging as an innovative method to engage students at all levels and improve learning and delivery effectiveness (Telner 2010). Here we begin with the related goal of designing medical applications to engage K-Grey in life-long learning. The development of games can be expensive (e.g. a high cost of maintaining intelligent systems). Thus, to deliver games with effectiveness, we provide a conceptual framework that helps relate types of knowledge, to games types that help achieve specific learning outcomes. The benefits of this Serious Game Framework (SGF) are 1) a conceptual architecture that considers the design issues, 2) support for multiple game types on the same architecture, and 3) the ability to assess, research and improve the learning process.
There are several challenges associated with the design of a serious game due to the underlying knowledge that must be structured and used to meet different goals. For example, a serious game might be used for the purpose of training students that get entertainment from the game; teachers who want to promote education; and even human computation roles where the goal is to outsource complex work such as tissue/pattern recognition to larger populations (von Ahn, 2008). Specifically, some of these goals are:
- •
Achieve a particular learning outcome.
- •
Relate the knowledge to different roles such as training, assessment, decision-making, and human computation2 roles.
- •
Achieve objectives like policy design and human computation or the ability to solve problems that cannot be solved effectively by computer (von Ahn, 2005).
- •
Overcome silos of different serious games where each serious game is currently separate, but there are requirements for the games to complement each other.
A framework is needed to conceptually integrate, analyze trade-offs and meet such goals.
We begin this paper with an overview of related research background concepts. Following this we will integrate this research into the Serious Game Framework and use a range of Medical Applications as examples of this framework. By applying the theories of learning and gaming to medical training, we illustrate how game design can help the cognitive, skill-based and affective learning and testing. We also present the underlying software architecture addressing issues like extensibility and scalability along with features for multiple designers. We conclude with research agendas in applications and computer science.
Top2 Background Concepts
To develop the Serious Game Framework (SGF) we first start with related knowledge structuring concepts. Much research has been done in related topics, ranging from learning strategies and outcomes (Wilson, 2009); to the design of games (Moreno-Ger,2008); to studies of principles to make games more attractive (Stewart,2009; Von Ahn,2005; Bates,2004); as well as the game attributes and its relationships to learning outcomes (Wilson, 2009). This too will be subsequently integrated into the framework.
Key Terms in this Chapter
Knowledge Type: Knowledge type is a classification of knowledge, which is categorized as declarative, procedural, and strategic knowledge in our case.
Human Computation: Human computation is any forms of computation process with participation of human.
Human Motivation: The psychological needs that drive players to play the serious game.
Learning Outcome: Learning outcome is the result of the learning process.
Serious Game: Serious game is any game that used for serious purpose.
Learning Object: Learning object is a small, self-contained and reusable entity that can be used for learning, education and training.
Serious Game Framework (SGF): SGF is a conceptual architecture that considers the design alternatives, support for multiple game types, and the ability to assess, research and improve the learning process.
Scape: Scape is environment for agents, which is a particular configuration of LOs, game type and learning outcome.
Agent: Agent is any autonomous entity which sense and response to the environments to achieve its goals.