Aligning Problem Solving and Gameplay : A Model for Future Research and Design
Aligning Problem Solving and Gameplay : A Model for Future Research and Design
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协调问题解决和游戏玩法：未来研究和设计的模型
DOI：
10.4018/978-1-61520-719-0.ch010
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发表时间：
2010
影响因子：
2.5
通讯作者：
Richard N. Van Eck
中科院分区：
文献类型：
--
作者：
Weoi Hung;Richard N. Van Eck
解决问题经常被讨论为游戏和基于游戏的学习的好处之一（例如，Gee，2007 a，货车Eck，2006 a），但几乎没有实证研究支持这一论断。建立和验证游戏中问题解决的模型至关重要（货车Eck，2007年），但如果没有比目前严肃游戏领域更好地理解问题解决，这将是困难的，甚至是不可能的。虽然游戏可以用来教授跨多个领域的各种内容（货车Eck，2006 b，2008），但游戏促进问题解决的能力对严肃游戏领域可能更重要，因为问题解决技能跨越所有领域，是最难实现的学习成果之一。这在科学、技术、工程和数学（STEM）中可能特别重要，这就是为什么严肃的游戏研究人员正在构建游戏来促进科学中的问题解决（例如，Gaydos & Squire，this volume;货货车Eck，Hung，Bowman，& Love，2009）.这也许就是为什么严肃游戏研究者正在构建游戏来促进科学中的问题解决的原因。当前严肃游戏的研究和设计理论不足以解释问题解决和游戏之间的关系，也不支持旨在促进问题解决的教育游戏的设计。问题解决和基于问题的学习（PBL）在欧洲和美国已经被深入研究了75年以上，虽然研究的重点和问题解决的概念化在这段时间里已经发展，但有大量的知识可以借鉴。最近，研究人员（例如，Jonassen，1997，2000，& 2002; Hung，2006 a; Jonassen & Hung，2008）在问题类型的划分和定义以及设计有效问题和PBL的模型方面都取得了进展。任何关于游戏和问题解决关系的模型和研究都必须建立在问题解决和PBL的现有研究基础上，而不是无意中覆盖这些领域的旧领域。在这一章中，我们提出了一个不同的问题，包括领域知识，结构化，以及它们相关的学习成果的维度的概述。然后，我们提出了一种游戏性分类（与游戏类型相反），该分类考虑了游戏过程中遇到的认知技能，部分依赖于以前的分类系统（例如，Apperley，2006），Mark Wolf（2006）的交互网格概念（我们称之为iGrid），以及我们自己的认知问题解决和游戏第2页对游戏性的分析。然后，我们使用这个分类系统，iGrid和示例游戏来描述11种不同类型的问题，它们的不同之处，以及最有可能支持它们的游戏类型。最后，我们描述了问题和游戏本身解决特定学习成果的能力，这些学习成果独立于问题解决，包括特定领域的学习、高阶思维、心理技能和态度改变。对未来研究的影响也进行了描述。我们相信，这种方法可以指导设计的游戏，旨在促进解决问题，并指出了未来的研究方向，在解决问题和游戏。
Problem solving is often discussed as one of the benefits of games and game-based learning (e.g., Gee, 2007a, Van Eck 2006a), yet little empirical research exists to support this assertion. It will be critical to establish and validate models of problem solving in games (Van Eck, 2007), but this will be difficult if not impossible without a better understanding of problem solving than currently exists in the field of serious games. While games can be used to teach a variety of content across multiple domains (Van Eck, 2006b, 2008), the ability of games to promote problem solving may be more important to the field of serious games because problem solving skills cross all domains and are among the most difficult learning outcomes to achieve. This may be particularly important in science, technology, engineering, and math (STEM), which is why serious game researchers are building games to promote problem solving in science (e.g., Gaydos & Squire, this volume; Van Eck, Hung, Bowman, & Love, 2009). This is perhaps why serious game researchers are building games to promote problem solving in science Current research and design theory in serious games are insufficient to explain the relationship between problem solving and games, nor do they support the design of educational games intended to promote problem solving. Problem solving and problem-based learning (PBL) have been studied intensely in both Europe and the United States for more than 75 years, and while the focus of that study and conceptualization of problem solving have evolved during that time, there is a tremendous body of knowledge to draw from. Most recently, researchers (e.g., Jonassen, 1997, 2000, & 2002; Hung, 2006a; Jonassen & Hung, 2008) have made advances in both the delineation and definition of problem types and models for designing effective problems and PBL. Any models and research on the relation of games and problem solving must build on the existing research base in problem solving and PBL rather than unwittingly covering old ground in these areas. In this chapter, we present an overview of the dimensions upon which different problems vary, including domain knowledge, structuredness, and their associated learning outcomes. We then propose a classification of gameplay (as opposed to game genre) that accounts for the cognitive skills encountered during gameplay, relying in part on previous classifications systems (e.g., Apperley, 2006), Mark Wolf's (2006) concept of grids of interactivity (which we call iGrids), and our own cognitive Problem Solving and Games p. 2 analysis of gameplay. We then use this classification system, the iGrids, and example games to describe eleven different types of problems, the ways in which they differ, and the gameplay types most likely to support them. We conclude with a description of the ability of problems and games themselves to address specific learning outcomes independent of problem solving, including domain-specific learning, higherorder thinking, psychomotor skills, and attitude change. Implications for future research are also described. We believe that this approach can guide the design of games intended to promote problem solving and points the way toward future research in problem solving and games.