I-Corps: Translation potential of learning engagement and assessment programs in multi-person virtual reality

这个i-Corps项目更广泛的影响是开发多人虚拟现实(MVR)中的学习、参与和评估软件应用程序。学习环境正准备经历一场重大的变革，在不久的将来，MVR可能被用来增强甚至可能取代传统的课堂学习环境。使用这项技术，学校可能会受益于能够获得关于学生行为的知识，特别是学习参与度。此外，该应用程序还可以用来教育用户大脑如何将概念或项目组织成网络。该解决方案还为用户提供了基于各种主题创建、保存和共享自己的网络的场景。这个i-Corps项目利用体验式学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。该解决方案基于多人虚拟现实(MVR)系统的开发，该系统允许通过非侵入性生物识别指标自动评估学生的学习投入。这些指标包括眼球运动特征、触觉相互作用和/或大脑额叶的血液动力学活动。具体地说，已经创建了MVR语义网络学习应用，以评估用户(学生)的学习参与度，衡量他们在MVR环境中对学习过程的积极参与。在学习过程中，根据学习任务实时同步各种多模式测量，然后自动计算和可视化每个用户的学习参与度水平。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this I-Corps project is the development of learning engagement and assessment software applications in multi-person virtual reality (MVR). The learning environment is poised to undergo a major reformation, and MVR may be used to augment, and possibly replace, the traditional classroom learning environment in the near future. Using this technology, schools may benefit by being able to gain knowledge on their students’ behaviors, especially the degree of learning engagement. In addition, the application may be used to educate users on how the brain organizes concepts or items into a network. The solution also provides scenarios for users to create, save, and share their own networks based on various themes.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a multi-person virtual reality (MVR) system that allows for the automatic assessment of students’ learning engagement through non-invasive biometric indicators. These indicators include eye movement characteristics, haptic interactions, and/or hemodynamic activities in the frontal lobes of the brain. Specifically, an MVR semantic network learning application has been created to assess users’ (students’) learning engagement, gauging their active involvement in the learning process within MVR environments. During the learning process, various multimodal measurements are synchronized in real-time in response to the learning task or tasks, then the learning engagement level of each user is automatically computed and visualized.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.