EAGER: Building a Foundation for Hands-on STEM Learning at a Distance: Pedagogical Agents for Embodied Education in Virtual Reality

Maker Education拥有重振数学、科学和工程教育并使其多样化的重大承诺，其结合了复杂技术实践的低门槛、丰富的社区资源和支持基础设施以及有趣的学习心态。然而，制造者教育最好在面对面进行，学习者和促进者可以讨论正在构建的构件，每个人都可以操作这些对象，他们可以使用手势来解释概念，他们可以自由调整他们对工作空间的观点。这使得扩大Maker教育的规模具有挑战性，因为它很难提供有效的远程教学，也很难创建教学资源。这项提议将开发具体化的教学代理--虚拟环境中的动画角色--可以在制造商的任务中提供便利，专注于电子电路设计。研究将首先在虚拟现实中建立一个互动语料库，在这个语料库中，专家辅导员和学习者看起来像是带有实时跟踪的动画化身，以驱动他们的动作并进行互动来构建电子电路。第二阶段将基于语料库分析和数据构建一个原型教学代理。在短期内，该项目将为STEM中代表性不足的群体的青年提供直接的教育利益，并将加强大学与社区的伙伴关系。在更长的时间尺度上，制定基于经验的促进行动目录可能会为教师教育和教学代理提供信息，从而更好地支持远程学习。该方法可扩展到一系列依赖于具体化互动的任务，如技能培训或物理治疗。这项提议将对学习和计算机科学做出重大贡献。由于创作者的促成往往依赖于教师的直觉决定，这并不是一个很好理解的过程。语料库将提供有关语言和非语言策略的重要数据，以促进制造者教育。对语料库和后来的代理与学习者交互的分析将提高我们对促进、非语言交流的作用以及如何在这些环境中指导学习的理解。构建代理的技术工作将建立教学代理在这一领域的效用。该语料库还将提供一个独特而丰富的数据集，在此基础上开发未来用于行为合成的机器学习算法。该数据集将包括语言和非语言行为，以及环境和交互对象的完整编码以及主持人应用的教学动作，超出了目前可用的任何内容。该奖项反映了NSF的法定使命，并已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。

Maker education holds significant promise to revitalize and diversify education in mathematics, science and engineering, with its combination of low barriers to entry for sophisticated technical practices, a rich community infrastructure of resources and support, and a playful learning mindset. However, maker education is best done in person, where the learner and facilitator can discuss the artifacts being built, each can manipulate those objects, they can use gesture to explain concepts and they can freely adjust their viewpoint on the workspace. This makes it challenging to scale maker education, because it is difficult to provide effective remote instruction and difficult to create instructional resources. This proposal will develop embodied pedagogical agents -- animated characters in a virtual environment -- that can provide facilitation on maker tasks, focusing on electronic circuit design. Research will begin by building a corpus of interactions in embodied virtual reality, where expert facilitators and learners appear as animated avatars with live tracking to drive their movements and interact to build electronic circuits. Phase two will build a prototype pedagogical agent based on the corpus analysis and data. In the near term, the project will provide direct educational benefit to youth from groups underrepresented in STEM and will strengthen university-community partnerships. Over longer time scales, the development of an empirically grounded catalog of facilitation moves has potential to inform teacher education and pedagogical agents can better support remote learning. The approach is scalable to a range of tasks that rely on embodied interaction, such as skills training or physical therapy. The proposal will make significant contributions to both learning and computer science. As maker facilitation often relies on intuitive decisions by the teacher, it is not a well understood process. The corpus will provide important data on the verbal and nonverbal strategies employed to facilitate Maker education. Analysis of the corpus and later agent-learner interactions will improve our understanding of facilitation, the role of nonverbal communication, and how learning is guided in these settings. The technical work to build the agent will establish the utility of pedagogical agents in this domain. The corpus will also provide a unique and rich dataset on which to develop future machine learning algorithms for behavior synthesis. The dataset will include verbal and nonverbal behavior, along with a full coding of the environment and interaction objects and teaching moves applied by the facilitator, exceeding anything currently available.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.