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The hierarchical learning network is envisioned to be a network of networks. Each of the component networks will link the NASA researchers and the university consortium with other government agencies, technology providers, and industry active in the selected areas. It will also significantly enhance the effectiveness of learning in these areas at predominantly minority institutions in the U.S., by enabling them to interact and learn from leading experts.
The hierarchical network will be configured as a neural network. Each of the component networks will integrate three learning environments: synchronous and asynchronous expert-led, self-paced, and collaborative. The three environments, in combination, are expected to reduce the cost of learning, and sustain and increase worker competencies at NASA and other high-tech organizations.
The human instructors in these environments will serve many roles, including inspiring, motivating, observing, evaluating, and steering the learners, both individually and in distributed teams. The human instructors in expert-lead distributed learning environment serve as coaches, guides, facilitators, and course managers. Their presentations focus on giving a broad overview of the topic and its diverse applications, and they end their presentations with more penetrating, what-if questions that can enhance the critical thinking and creativity of the learners. Elaborate visualization and multimedia facilities are used in presentations and will be developed in selected areas, under this grant. Routine instructional and training tasks are relegated to the self-paced individual environment.
The individual learning environment engages the learner and provides a high degree of tailored interaction. It can be used for self-paced instruction of routine material not covered in the lecture. Such instruction can be enhanced by using virtual instructors (intelligent software agents) assigned by the human instructors. It can be used to study physical phenomena that can be coupled with engineering processes, using advanced visualization (e.g., animations, 3-D models and QuickTime VR), multimedia, and multisensory immersive facilities. The individual learning environment can serve to carry out virtual experiments - computer simulation of physical experiments. This includes the development of interactive virtual labs (wind tunnels, structures, and acoustic labs) for education and training. Intelligent software agents, interrogative visualization engines and novel paradigms such as computational steering (near real-time simulation, visualization and control), and inverse steering will be used in these facilities.
Collaborative learning environments teach teamwork and group problem solving. Instructors and learners can be geographically dispersed. Eventually, they can be brought together through immersive tele-presence facilities to share their experiences in highly heterogeneous environments involving different computing platforms, software, and other facilities, and they will be able to work together on designing complex engineering systems, beyond what is traditionally done in academic settings. Because participants can be virtually collocated without leaving their industry and government laboratories, collaborative learning environments can enable the formation of new university, industry, and government consortia. Collaborative learning environment will also provide engineering schools with the facility to offer multiuniversity courses with group distributed instructors, each being an expert in the subject matter of his/her lectures.
The multisensory representation of complex engineering data through the combined use of visualization, haptic feedback, and sonfication (the use of nonspeech audio to convey information) will significantly increase the bandwidth of the human-computer interface, and help in revealing underlying features that go unnoticed when only the mode of visualization is applied. The currently used WIMP (windows, icons, menus, pointing systems) interfaces will be replaced by knowledge-user-interfaces, such as voice-activated personal-access devices with wireless communication, speed reading, and visual browsers, for improving communication and search capability.
The coupling of intelligent agents with feature recognition can lead to the development of a virtual instructor that recognizes reactions communicated, for example, through the facial expression and the vocal intonation of the learner, and provides an appropriate response. The learner can engage the virtual instructor in a two-way conversation in natural language, asking for details or background for the material covered. The interaction with the virtual instructors is more like face-to-face interaction and goes beyond menu-based and text-based interfaces. In addition, the advances in human-machine communication and cognitive neuroscience might provide the virtual instructor with more insight and understanding of human thought development. The virtual instructor can motivate learners emotionally as well as rationally. Emotion plays an important role in learning. The virtual instructor can free human instructors from the routine tasks associated with information transfer.
The component networks of the hierarchical network represent the synergistic coupling of advanced instructional, communication, knowledge management, assessment and evaluation technologies. The technical content used in the environment is generated as modules developed by experts in the subject matter. Each module will use interrogative visualization, multimedia, intelligent software agents, virtual reality, multimodal and adaptive human-computer/communication technologies (including perceptual user interface and natural language communication).