In this article, we present the application of a method for visualizing gameplay patterns observed in log-file data from a geometry game. Using VisCareTrails, a data visualization software system based on the principle of timed word trees, we were able to identify five novel behaviors that informed our understanding of how players were approaching the game. We further utilized these newly identified player behaviors by triangulating them with geometry test scores collected from players outside the game setting. We compared the predictive capacity of these behaviors against five demographic characteristics commonly observed to be associated with educational outcomes: age, gender, ethnicity, mother’s education, and attitude toward video games. Two of the novel behaviors we identified, both reflecting inflexible problem-solving strategies, outperformed all demographic variables except age in terms of predicting change in geometry test scores post-gameplay. We believe that this is sound evidence for the utility of VisCareTrails and the timed-word-tree method for identifying pedagogically relevant player behaviors from semi-structured data associated with educational games.

中文翻译：

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使用定时词树可视化来自游戏的日志文件数据

在本文中，我们展示了一种方法的应用，该方法用于可视化在几何游戏的日志文件数据中观察到的游戏玩法模式。使用 VisCareTrails，这是一个基于定时词树原理的数据可视化软件系统，我们能够识别五种新颖的行为，让我们了解玩家如何接近游戏。我们通过使用从游戏设置之外的玩家收集的几何测试分数对这些新发现的玩家行为进行三角测量，进一步利用了这些新发现的玩家行为。我们将这些行为的预测能力与通常观察到的与教育成果相关的五个人口统计特征进行了比较：年龄、性别、种族、母亲的教育和对电子游戏的态度。我们发现的两种新行为，都反映了僵化的解决问题的策略，在预测游戏后几何测试分数的变化方面，其表现优于除年龄以外的所有人口统计变量。我们相信，这是 VisCareTrails 和定时词树方法的实用性的可靠证据，用于从与教育游戏相关的半结构化数据中识别与教学相关的玩家行为。