Adaptive Expertise in Second Life

第二人生的适应性专业知识

• 批准号: 0745694
• 负责人: Philip Vahey
• 金额: --
• 依托单位: SRI International
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0745694&HistoricalAwards=false
• 关键词: Adaptive; Expertise; Second; Life

This project will (a) investigate how to best leverage the unique affordances of virtual worlds such as Second Life in to investigate the sociocognitive processes involved in innovation; and (b) create a research infrastructure to greatly advance the capacity in the learning sciences to conduct research in virtual worlds such as Second Life (SL). Key to (a) is refining and expanding an existing early-stage SL-based activity that will elicit innovative group behaviors. Key to (b) is exploring and addressing the highly technical challenges in ?instrumenting? the Second Life task environment and analyzing data using automated data analysis tools. This automated data analysis will correlate expert ratings, derived from manually applied ratings developed through an evidence-centered design process, with results of latent semantic analysis techniques applied to group process and product data. This research infrastructure will be tested through a pilot study with five groups, each consisting of four individuals. The proposed research is high-risk given the difficulty in automatically collecting data on activity and communication in the virtual environment; many technical challenges related to implementing automated data collection will need to be resolved. In addition, the technology innovation is constrained by the requirements of learning sciences research as well as by the constraints of the technical capabilities, social norms and user expectations associated with the SL environment, and requirements of other applications with which the SL platform will be used.The use of virtual worlds to create innovative teaching and learning environments shows great promise. This, coupled with the explosive growth in the use of virtual worlds generally and Second Life (SL) in particular for social, business, and learning purposes, drives a need to increase capacity in the learning sciences to use such environments for learning and research. This project will contribute to knowledge about the use of virtual worlds for research and learning in the following ways: (1) it will develop methods for automatically capturing data people?s (avatars?) and objects? behavior inside virtual worlds, including avatar-avatar interactions and avatar-object interactions; then (2) use this instrumented SL environment as a research tool to study characteristics of social interaction in the context of group collaborations that are associated with innovation; and (3) analyze the unique affordances of virtual worlds as learning environments. In addition, we will explore the feasibility of using latent semantic analysis, a statistical approach to analyzing the content of texts, to analyze characteristics of groups? collaborative problem solving associated with innovation. This project is high risk because technical challenges in implementing automated data capture in SL need to be addressed, and there is a lack of existing exemplars or published research on such methods.

该项目将（a）研究如何最好地利用虚拟世界（如第二人生）的独特启示，以研究创新中涉及的社会认知过程;（B）创建一个研究基础设施，以大大提高学习科学在虚拟世界（如第二人生）中进行研究的能力。（a）的关键是改进和扩展现有的早期SL活动，以激发创新的群体行为。（B）的关键是探索和解决高度技术性的挑战？仪器？第二人生任务环境和使用自动数据分析工具分析数据。这种自动化的数据分析将把专家评级与潜在语义分析技术的结果相关联，专家评级来自通过以证据为中心的设计过程开发的手动应用评级，潜在语义分析技术应用于组过程和产品数据。 这一研究基础设施将通过一项试点研究进行测试，有五个小组，每个小组由四名个人组成。鉴于在虚拟环境中自动收集活动和通信数据的困难，拟议的研究是高风险的;与实施自动数据收集有关的许多技术挑战需要解决。此外，技术创新受到学习科学研究的要求以及与SL环境相关的技术能力、社会规范和用户期望的约束，以及SL平台将被使用的其他应用的要求的约束。使用虚拟世界来创建创新的教学和学习环境显示出巨大的前景。这一点，再加上爆炸性增长的使用虚拟世界一般和第二人生（SL），特别是为社会，商业和学习的目的，驱动需要提高能力的学习科学使用这种环境的学习和研究。该项目将有助于知识的使用虚拟世界的研究和学习在以下方面：（1）它将开发自动捕获数据的人的方法？s（化身？）和对象？虚拟世界内的行为，包括化身-化身交互和化身-对象交互;然后（2）使用这种仪器SL环境作为研究工具，研究与创新相关的群体协作背景下的社会交互特征;（3）分析虚拟世界作为学习环境的独特启示。此外，我们将探讨潜在语义分析，一种统计方法来分析文本的内容，分析群体的特征的可行性？与创新相关的协作问题解决。这个项目是高风险的，因为在SL中实现自动化数据采集的技术挑战需要解决，并且缺乏现有的范例或已发表的研究。