Cooperative design has been an integral part of many games. With the success of games like Left4Dead, many game designers and producers are currently exploring the addition of cooperative patterns within their games. Unfortunately, very little research investigated cooperative patterns or methods to evaluate them. In this paper, we present a set of cooperative patterns identified based on analysis of fourteen cooperative games. Additionally, we propose Cooperative Performance Metrics (CPM). To evaluate the use of these CPMs, we ran a study with a total of 60 participants, grouped in 2-3 participants per session.Participants were asked to play four cooperative games (Rock Band 2, Lego Star Wars, Kameo, and Little Big Planet). Videos of the play sessions were annotated using the CPMs, which were then mapped to cooperative patterns that caused them. Results, validated through inter-rater agreement, identify several effective cooperative patterns and lessons for future cooperative game designs.

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__________________________________________________________________________________________There has been a recent increase in the number of game environments or engines that allow users to customize their gaming experiences by building and expanding game behavior. This article describes the use of modifying, or modding, existing games as a means to learn computer science, mathematics, physics, and aesthetic principles. We describe two exploratory case studies of game modding in classroom settings to illustrate skills learned by students as a result of modding existing games. We also discuss the benefits of learning computer sciences skills (e.g., 3D graphics/mathematics, event-based programming, software engineering, etc.) through large design projects and how game design motivates students to acquire and apply these skills. We describe our use of multiple game modding environments in our classes. In addition, we describe how different engines can be used to focus students on the acquisition of particular skills and concepts. INTRODUCTIONIn the late 1990s, game developers began to separate gaming experiences (e.g., rules, behaviors, characters) from the underlying engines that power them. These game engines allow designers to create new game behaviors and graphics by plugging into reusable architectures that handle polygon rendering, camera control, lighting, and so on. Many popular game engines come with scripting languages that allow users to modify their behaviors, create new worlds for exploration, or even modify existing games into completely new ones. This process, often referred to as modding, still requires designers to understand how to communicate with game engines, but the overhead of creating a working product is significantly less than working without the functionality that these engines provide.Modding offers a number of advantages over designing games from scratch [Emmerson 2004]. The time and costs related to video game development are enormous, preventing most individuals from being able to create games that resemble those built by corporate designers. However, modifying existing games is within the reach of many game players, as companies provide tools to allow designers to tweak their games. Since modding begins with popular, proven game concepts, the resulting variations are more likely to resemble games that players/modders are accustomed to than if they had to build entire game infrastructures on their own.

Understanding players' visual attention patterns within an interactive 3D game environment is an important research area that can improve game level design and graphics. Several graphics techniques use a perception based rendering method to enhance graphics quality while achieving the fast rendering speed required for fast-paced 3D video games. Game designers can also enhance game play by adjusting the level design, texture and color choices, and objects' locations, if such decisions are informed by a study of players' visual attention patterns in 3D game environments. This paper seeks to address this issue. We present results showing different visual attention patterns that players exhibit in two different game types: action-adventure games and first person shooter games. In addition, analyzing visual attention patterns within a complex 3D game environment presents a new challenge because the environment is very complex with many rapidly changing conditions; the methods used in previous research cannot be used in such environments. In this paper, we will discuss our exploration seeking a new approach to analyze visual attention patterns within interactive 3D environments.

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Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Warping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: Boom-Town, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.

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The game improved considerably from its initial conception through three stages of iterative design and feedback. Our assessment methods for each stage targeted and integrated learning, health, and engagement outcomes. Lessons learned through this iterative design process are a great contribution to the games for health community, especially in targeting the development of health and learning goals through game design.