Adaptive Expertise in Second Life

该项目将(A)研究如何最好地利用虚拟世界如“第二人生”的独特优势，研究创新所涉及的社会认知过程；(B)建立一个研究基础设施，以极大地提高学习科学在“第二人生”等虚拟世界中进行研究的能力。(A)的关键是提炼和扩展现有的基于SL的早期活动，这将引发创新的群体行为。(B)的关键是探索和解决仪表方面的高技术挑战？第二人生任务环境，并使用自动化数据分析工具分析数据。这种自动化的数据分析将把通过以证据为中心的设计过程制定的手动应用评级得出的专家评级与应用于集团工艺和产品数据的潜在语义分析技术的结果相关联。这一研究基础设施将通过五个小组的试点研究进行测试，每个小组由四个人组成。鉴于难以自动收集关于虚拟环境中的活动和通信的数据，拟议的研究风险很高；需要解决与实施自动化数据收集有关的许多技术挑战。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以及SL平台将与其他应用程序一起使用的要求的约束，利用虚拟世界来创造创新的教学环境显示出巨大的潜力。这一点，再加上虚拟世界和第二人生(SL)的爆炸性增长，特别是用于社会、商业和学习目的，促使学习科学需要提高能力，以利用这种环境进行学习和研究。该项目将从以下几个方面促进有关使用虚拟世界进行研究和学习的知识：(1)它将开发自动捕获数据人的方法？S(化身？)那物品呢？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)将这个仪表化的SL环境作为研究工具，研究与创新相关的群体合作背景下的社会交互特征；以及(3)分析虚拟世界作为学习环境的独特提供。此外，我们还将探索使用潜在语义分析的可行性，这是一种分析文本内容的统计方法，用于分析群体特征？与创新相关的协作性问题解决。这个项目风险很高，因为需要解决在SL中实现自动数据捕获的技术挑战，而且缺乏关于这种方法的现有范例或已发表的研究。

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.