Understanding player strategies is a key question when analyzing player behavior both for academic researchers and industry practitioners. For game designers and game user researchers, it is important to gauge the distance between intended strategies and emergent strategies; this comparison allows identification of glitches or undesirable behaviors. For academic researchers using games for serious purposes such as education, the strategies adopted by players are indicative of their cognitive progress in relation to serious goals, such as learning process. Current techniques and systems created to address these needs present a few drawbacks. Qualitative methods are difficult to scale upwards to include large number of players and are prone to subjective biases. Other approaches such as visualization and analytical tools are either designed to provide an aggregated overview of the data, losing the nuances of individual player behaviors, or, in the attempt of accounting for individual behavior, are not specifically designed to reduce the visual cognitive load. In this work, we propose a novel visualization technique that specifically addresses the tasks of comparing behavior sequences in order to capture an overview of the strategies enacted by players and at the same time examine individual player behaviors to identify differences and outliers. This approach allows users to form hypotheses about player strategies and verify them. We demonstrate the effectiveness of the technique through a case study: utilizing a prototype system to investigate data collected from a commercial educational puzzle game. While the prototype's usability can be improved, initial testing results show that core features of the system proved useful to our potential users for understanding player strategies.
翻译:在分析学术研究人员和行业从业人员的玩家行为时,了解玩家战略是一个关键问题。对于游戏设计者和游戏用户研究人员来说,重要的是要测量预定战略和突发战略之间的距离;这种比较有助于辨别故障或不良行为。对于为教育等严肃目的使用游戏的学术研究人员来说,玩家采取的战略表明他们与学习过程等严肃目标相比的认知进展。目前为解决这些需要而创建的技术和系统有几个缺点。定性方法很难向上扩展,以包括众多玩家,并容易出现主观偏差。其他方法,例如视觉化和分析工具,或者旨在提供数据的综合概览,失去个别玩家行为的细微差别,或者在计算个人行为时,并不是专门设计来减少视觉认知负担的。在这项工作中,我们建议一种新颖的视觉化技术,专门处理比较行为序列的任务,以便了解玩家制定的战略的有用性,同时检查个别玩家的行为,以辨别差异和偏差。这个方法可以让用户对数据进行综合概述,同时从商业游戏的特性进行初步分析,我们也可以从游戏的模型研究中找出一个模型研究。我们所收集的游戏的模型研究。我们所收集的学习的游戏的游戏的模型,可以用来来证明的游戏的模型研究。