MRI: Acquisition of CAVE for Experiments in the Creation of Collaborative Learning Environments

MRI：收购 CAVE 用于创建协作学习环境的实验

• 批准号: 0420996
• 负责人: Lori Scarlatos
• 金额: $ 29.98万
• 依托单位: CUNY Brooklyn College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0420996&HistoricalAwards=false
• 关键词: MRI; Acquisition; CAVE; Experiments; Creation

This project, creating learning environments where children can be fully immersed in and engaged with their learning materials, aims at acquiring high-end visualization and motion capture in the form of the Tangible Interfaces Collaborative Learning Environments (TICLE) project. The project centers around the development of learning environments; techniques for handling multi-sensory cues in user interfaces, and the use of visual technology in learning and adaptation in intelligent agents. While studying interfaces amenable for human computer interaction and learning, proposed is the acquisition of a CAVE-based immersive environment as well as a motion capture system that will be integrated with it. Exploring techniques such as virtual reality, full motion tracking, and sensor input devices, the work involves working on gesture tracking and recognition/ interpretation, determining how to relate gestures to manipulation of objects. The proposal addresses the following research directions:Tangible Interfaces for Collaborative Learning EnvironmentsInteraction ParadigmsTracking and Representing the Learning EnvironmentRectifying and Resolving Conflicts among Data SourcesMulti-Parametric Interfaces and Complex Multi-Media Control SituationsIncreasingly Multi-Parametric Real-Time Multi-Media Control SituationsUser Feedback Strategies for Multi-Parametric Real-Time Multi-Media Control SituationsTraining and Learning Multi-Parametric Real-Time Multi-Media ControlLearning and Adaptation in Intelligent AgentsLearning and Adaptation by Artificial Intelligent AgentsLearning and Adapting Human AgentsLearning and Adapting as a Programming MetaphorSeveral subprojects extend previous work into a 3D cave environment, requiring a significant jump in complexity of the system including computer vision, graphics, and systems; as well as quite a bit of training. For the latter project, a motion tracking system monitors the robots and the CAVE to give feedback to the robots. In the area of human computer interfaces (HCI), strategies will be developed for tracking and representing what is going on in the learning environment, including algorithms for rectifying and resolving conflicts among disparate sources (such as multiple pairs of cameras and sensors). Experimentation with various paradigms determines how students may best interact with the objects and information. Ways of interacting with the space and the data in natural, expressive ways, affecting multiple parameters will be explored. In the area of artificial intelligence, the focus will be in determining what the users understand or intend. The investigation of adaptive agents will continue, allowing the system to fill in missing data and learn over time. All tracks focus on how the HCI can enhance collaborative educational environments. Broader Impact: Supporting research at a minority institution, the work involves undergraduate and graduate students, including those from underrepresented groups. Exposing a wider audience into science, the work on TICLE exhibits high potential for broader impact in education. Moreover, outreach to K-12 is already under way.

该项目旨在创建学习环境，使儿童能够完全沉浸在学习材料中并参与其中，旨在以TICLE项目的形式获得高端可视化和动作捕捉。该项目围绕学习环境的开发;处理用户界面中多感官提示的技术，以及在智能代理中使用视觉技术进行学习和适应。在研究人机交互和学习接口的同时，提出了一种基于CAVE的沉浸式环境以及与之集成的运动捕捉系统，探索虚拟现实，全运动跟踪和传感器输入设备等技术，工作涉及手势跟踪和识别/解释，确定如何将手势与物体的操作联系起来。该提案涉及以下研究方向：协作学习环境的TAPE接口交互范例学习环境的跟踪和表示纠正和解决数据源之间的冲突多参数接口和复杂的多媒体控制情境越来越多的多参数实时多媒体控制情境多参数实时多媒体控制情境的用户反馈策略训练和学习多参数实时多媒体控制情境媒体控制智能主体的学习和适应人工智能主体的学习和适应人类主体的学习和适应作为编程隐喻的学习和适应子项目将以前的工作扩展到3D洞穴环境中，需要系统复杂性的显着飞跃，包括计算机视觉，图形和系统;以及相当多的培训。对于后一个项目，运动跟踪系统监视机器人和CAVE，以向机器人提供反馈。在人机界面（HCI）领域，将开发用于跟踪和表示学习环境中正在发生的事情的策略，包括用于纠正和解决不同来源（如多对摄像机和传感器）之间冲突的算法。各种范例的实验决定了学生如何最好地与物体和信息进行交互。将探索如何以自然，富有表现力的方式与空间和数据进行交互，影响多个参数。在人工智能领域，重点将是确定用户的理解或意图。对自适应代理的研究将继续进行，允许系统填补缺失的数据并随着时间的推移进行学习。所有的轨道都集中在HCI如何增强协作教育环境。更广泛的影响：支持在少数民族机构的研究，工作涉及本科生和研究生，包括那些来自代表性不足的群体。TICLE的工作使更多的受众了解科学，显示出在教育方面产生更广泛影响的巨大潜力。此外，已经开始向K-12推广。