This paper describes the design, evaluation, and lessons learned from a project involving the implementation of an immersive virtual environment for children called NICE (Narrative-based, Immersive, Constructionist/Collaborative Environments). The goal of the NICE project was to construct a testbed for the exploration of virtual reality as a learning medium within the context of the primary educational reform themes of the past three decades. With a focus on informal education and domains with social content, NICE embraces the constructivist approach to learning, collaboration, and narrative development, and is designed to utilize the strengths of virtual reality: a combination of immersion, telepresence, immediate visual feedback, and interactivity. Based on our experiences with a broad range of users, the paper discusses both the successes and limitations of NICE and concludes with recommendations for research directions in the application of immersive VR technologies to children's learning.

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Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph-based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time.In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.

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Pain management of end of life patients (EOL) (n=596 episodes) is examined using statistical and data mining processes of the HANDS database of care plans coded with NANDA-I, NOC, and NIC (NNN) terminologies. HANDS episode data (episode =care plans updated at every handoff on a patient while staying on a hospital unit) were gathered in 8 units located in 4 different health care facilities (total episodes = 40,747; EOL episodes = 1,425) over two years. Results show the multiple discoveries such as EOL patients with hospital stays (< 72 hrs.) are less likely (p<0.005) to meet the pain relief goals compared to EOL patients with longer hospital stays. The study demonstrates a major benefit of systematically integrating NNN into electronic health records.