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

途径：大型替代现实教育游戏——评估表现和游戏

• 批准号: 1224088
• 负责人: Yu-Han Chang
• 金额: $ 25万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1224088&HistoricalAwards=false
• 关键词: Pathways; Large; Alternate; Reality; Games

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.

南加州大学（USC）将在先前工作的基础上测试一个强大的模型，用于评估增强现实游戏（ARG）中的玩家内容参与和社交互动。通过与No Mimes Media合作，USC将使用机器学习算法来自动进行玩家推理，以定制游戏玩法。游戏的内容重点将涵盖一系列STEM学科，特别强调地球科学内容和科学调查实验推理。来自洛杉矶服务不足社区的高中青年将被招募参加这项奋进。这个途径项目将使用各种“兔子洞”技术，以吸引大一和大二的学生从合作伙伴特许学校的在线游戏。兔子洞策略可能包括神秘的海报，好奇的标志，SQR代码战略性地放置在目标群体的平原和开放的视野。该游戏将完全访问的目标群体在线。在ARG体验期间，年轻玩家将遇到STEM概念和科学问题。敌对角色将传播科学误解和非科学推理，并挑战玩家运用他们的科学知识和技能来升级，获得徽章，并在游戏中移动。随着游戏的持续进行，机器学习算法将收集有关玩家学习能力和游戏中社交互动的数据。将对这些数据进行汇总和分析，以评估ARG环境中的学习和互动。 额外的分析将通过混合方法进行，外部评估小组CRESST将用于项目的形成性和总结性评估。预计约有300名目标年级的青少年将参与游戏体验。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，该项目的潜在影响范围可能会大得多。此外，该项目的既定目标不仅是为广泛的实施提供可扩展的模型，而且还努力为木偶大师提供研究和评估工具，为ARG环境中的玩家创造更个性化的体验和改善的学习成果。