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.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Ephraim P. Glinert
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Sri International
Menlo Park
United States
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