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

I-Corps项目的更广泛影响是在多人虚拟现实（MVR）中开发学习参与和评估软件应用。学习环境正准备经历一场重大改革，在不久的将来，MVR可能会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，学校可以通过获得学生行为的知识，特别是学习参与程度而受益。此外，该应用程序还可用于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参与度。这些指标包括眼动特征、触觉相互作用和/或大脑额叶的血流动力学活动。具体来说，我们创建了一个MVR语义网络学习应用程序来评估用户（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，各种多模态测量实时同步响应学习任务或任务，然后自动计算和可视化每个用户的学习参与程度。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.