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

点击翻译按钮获取中文摘要

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.