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

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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.