Data-driven gameplay experience evaluation: Leveraging multimodal data in Games User Research

近年来，数字游戏的开发和研究发生了许多变化，包括新的互动模式、扩大的玩家群体和新的商业模式。这些既有机遇，也有更多的不确定性。因此，在过去的几年里，对涉及用户及其与数字游戏互动的研究研究的需求有所增长。游戏用户研究(GUR)是一个相对较新的领域，利用人机界面和心理学的评估方法，旨在通过为研究人员和设计者提供关于游戏玩性的数据驱动信息来改进游戏设计。尽管标准的人机界面评估方法在理解生产力应用程序的可用性方面取得了进展，但视频游戏的特定特征，如只要它是游戏设计的一部分，就允许沮丧，这意味着许多已建立的用户研究方法不能以同样的方式应用于GUR。此外，游戏用户研究人员通常要处理海量和复杂的多模式数据集。因此，其中一个挑战是提高数据收集方法的效率，并在研究和设计决策中对这些数据进行有意义的解释。基于这些原因，优化适合游戏与人机交互研究的评估方法已成为游戏和人机交互研究人员的关键研究课题之一(如CHI 2019“游戏中的用户体验研究”课程、CHI 2016“独立和非营利性组织的GUR”研讨会和CHI 2020游戏与游戏特别兴趣小组(SIG Meet)：“塑造游戏和人机研究的下一个十年”)。我的研究计划集中在这些关键的研究主题上，通过开发工具和框架来使用系统和科学的方法来分析玩家的体验。这一建议解决了应用GUR设置中的一个重要问题，即通过利用多模式游戏数据(如玩家通过采访进行评论，他们在游戏中的动作和通过遥测进行的移动)来改善对玩家体验的评估。这项研究的主要成果将包括玩家体验评估的新方法，以及基于玩家体验的实际案例研究和科学实验结果生成的最佳实践和游戏设计指南。这里提出的工作分为两个研究要素：第一，通过探索现有的方法，我计划开发混合方法，以提高定性和定量数据收集、分析和可视化的有效性和效率。第二，应用数据驱动的设计和决策来推进混合现实(增强现实和虚拟现实)和流媒体(例如eSports)内容的游戏开发框架和设计指南。因此，这项研究的结果将包括适用的工具和方法以及优化游戏行业的人机交互(HCI)方法的最佳实践。

In recent years, there have been many changes in digital games development and research, including new interaction modes, widening player demographics and new business models. These present opportunities, but also additional uncertainties. As a result, the demand for research studies dealing with users and their interactions with digital games has grown in the past few years. Games user research (GUR) is a relatively new field using evaluation methods from HCI and psychology aiming to improve game design by providing data-driven information about gameplay for researchers and designers. Although standard HCI evaluation methods have made progress in understanding the usability of productivity applications, the specific characteristics of video games, such as frustration being allowed as long as it is part of the game design, mean that many established methods of user research cannot be applied in the same way for GUR. Moreover, games user researchers are usually dealing with massive and complex multimodal datasets. Thus, one of the challenges is in improving the efficiency of data gathering approaches and in interpreting these data meaningfully in research and in making design decisions. For these reasons, optimisation of evaluation methods suitable for GUR has become one of the key research topics for games and HCI researchers (as noted in various GUR related events such as the CHI 2019 Course on "UX Research in Games", the CHI 2016 Workshop on "GUR for Indie and non-profit organisations", and CHI 2020 Games & Play Special Interest Group (SIG meeting): "Shaping the Next Decade of Games and HCI Research"). My research program focuses on these key research topics by developing tools and frameworks for analysing player experience using systematic and scientific approaches. This proposal tackles an important problem in applied GUR setting, which is to improve evaluation of player experience by leveraging multimodal gameplay data (such players comments via interview, their in-game actions and movement via telemetry). Major outcomes of this research will include novel approaches for player experience evaluation as well as best practices and game design guidelines generated based on results from practical case studies and scientific experiments of player experience. The work proposed here is divided into two research elements: First, by exploring current approaches I plan to develop mixed methods that improve the effectiveness and efficiency of qualitative and quantitative data collection, analysis and visualization. Second, applying data-driven design and decision making to advance game development frameworks and design guidelines in mixed-reality (augmented and virtual reality) and streaming (e.g. eSport) content. Hence the outcome of this research will include applicable tools and methods as well as best practices on optimisation of human-computer interaction (HCI) methods for the game industry.