Pathways: Large Alternate Reality Games for Education -- Assessing Performance and Play

南加州大学(USC)将在之前工作的基础上，测试一个健壮的模型，用于评估增强现实游戏(ARG)中的玩家内容参与度和社交互动。与No Mimes Media合作，南加州大学将使用机器学习算法来自动进行玩家推理，以定制游戏。游戏的内容重点将跨越一系列STEM学科，特别强调地球科学内容和科学调查与实验推理。来自洛杉矶服务不足社区的高中生将被招募参与这一努力。这个Path项目将使用各种“兔子洞”技术来吸引来自合作特许学校的一年级和二年级的学生来玩这款在线游戏。兔子洞战略可能包括神秘的海报，好奇的标志，以及战略性地放置在目标群体的平淡和开放的视野中的SQR代码。这款游戏将完全向目标群体在线开放。在ARG体验期间，青少年球员将遇到STEM概念和科学问题。敌对角色将传播科学的误解和非科学的推理，并挑战玩家使用他们的科学知识和技能来升级，获得徽章，并通过游戏。随着游戏的继续，机器学习算法将收集玩家在游戏中学习能力和社交互动的数据。这些数据将被汇总和分析，以评估ARG环境中的学习和互动。外部评估小组CRESST将采用混合方法进行其他分析，以进行项目形成性和终结性评估。预计将有大约300名处于目标年级水平的青年参加游戏体验。然而，考虑到游戏和评估工具的使用范围将扩大到目标群体之外，该项目的潜在影响可能会更大。此外，该项目的既定目标不仅是为广泛实施产生一个可扩展的模型，而且还努力为木偶大师提供研究和评估工具，以在ARG环境中为玩家创造更个性化的体验和改进的学习结果。

The University of Southern California (USC) will build on prior work to test a robust model for assessing player content engagement and social interactions within an augmented reality game (ARG). In partnership with No Mimes Media, USC will use machine learning algorithms to make automated player inferences to customize game play. The content focus of the game will span a range of STEM disciplines, with a special emphasis on earth science content and scientific investigation & experimentation reasoning. High school youth from underserved communities in Los Angeles will be recruited to participate in the endeavor. This pathways project will use various "rabbit hole" techniques to attract freshmen and sophomore students from partner charter schools to the online game. The rabbit hole strategies may include cryptic posters, inquisitive signs, & SQR codes strategically placed in plain and open view of the target group. The game will be fully accessible to the target group online. During the ARG experience, youth players will encounter STEM concepts and scientific problems. Antagonistic characters will promulgate scientific misconceptions and nonscientific reasoning and challenge players to employ their scientific knowledge and skills to level-up, gain badges, and move through the game. As game play persists, machine learning algorithms will gather data on the players learning competencies and social interactions within the game. These data will be aggregated and analyzed to assess learning and interactions within the ARG environment. Additional analyses will be conducted by the mixed methods approach the external evaluation group, CRESST, will employ for the project formative and summative evaluations.Approximately 300 youth, within the target grade levels, are expected to participate in the gaming experience. However, given that access to the game and assessment tools will expand beyond the target group, the potential reach of the project could be much greater. Further, the stated aim of the project is not only to produce a scalable model for broad implementation but it also endeavors to provide puppetmasters with research and assessment tools to create more individualized experiences and improved learning outcomes for players within ARG environments.