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）的独特功能来研究涉及创新的社会认知过程；(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)的关键是精炼和扩展现有的早期阶段基于sl的活动，这将引发创新的群体行为。(b)的关键是探索和解决在仪器仪表方面的高度技术性挑战。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专家评级与应用于组过程和产品数据的潜在语义分析技术的结果相关联，专家评级来自于通过以证据为中心的设计过程开发的手动应用评级。这一研究基础设施将通过一项有五个小组的试点研究进行测试，每个小组由四个人组成。考虑到在虚拟环境中自动收集活动和通信数据的困难，所提出的研究是高风险的；需要解决与实现自动化数据收集相关的许多技术挑战。此外，技术创新不仅受到学习科学研究需求的约束，还受到与SL环境相关的技术能力、社会规范和用户期望的约束，以及使用SL平台的其他应用需求的约束。使用虚拟世界来创造创新的教学和学习环境显示出巨大的前景。再加上虚拟世界和Second Life （SL）在社交、商业和学习目的方面的爆炸性增长，推动了学习科学中增加使用这种环境进行学习和研究的能力的需求。这个项目将在以下几个方面对使用虚拟世界进行研究和学习的知识作出贡献：(1)它将开发自动捕获数据人的方法？S（化身？）和物体？虚拟世界中的行为，包括化身与化身的互动和化身与对象的互动；然后(2)将这种工具化的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互动特征；(3)分析虚拟世界作为学习环境的独特功能。此外，我们将探索使用潜在语义分析（一种分析文本内容的统计方法）来分析群体特征的可行性。与创新相关的协作解决问题。这个项目是高风险的，因为需要解决在SL中实现自动数据捕获的技术挑战，并且缺乏关于此类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.