论文标题
利用集群分析来了解教育游戏玩家的体验和支持设计
Leveraging Cluster Analysis to Understand Educational Game Player Experiences and Support Design
论文作者
论文摘要
教育游戏设计师了解观众的游戏风格和由此产生的体验的能力是改善游戏设计的重要工具。由于游戏受到大规模玩家测试的进行，设计人员需要廉价的自动化方法来对玩家游戏互动的模式进行分类。在本文中，我们提出了一个简单，可重复使用的过程，该过程使用最佳实践进行数据聚类，可在小型教育游戏工作室中使用。我们利用该方法来分析实时策略游戏，处理游戏遥测数据，以根据游戏中的动作，收到的反馈以及在游戏中的进度来确定玩家的类别。对这些集群的解释性分析导致对游戏设计师的可行见解。
The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio. We utilize the method to analyze a real-time strategy game, processing game telemetry data to determine categories of players based on their in-game actions, the feedback they received, and their progress through the game. An interpretive analysis of these clusters results in actionable insights for the game's designers.
