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

创客教育具有振兴数学，科学和工程教育并使其多样化的重要前景，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，创客教育最好是亲自进行，学习者和促进者可以讨论正在构建的工件，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，他们可以自由地调整他们对工作空间的看法。 这使得规模制造商教育具有挑战性，因为它很难提供有效的远程教学，也很难创建教学资源。 该提案将开发具体的教学代理-在虚拟环境中的动画角色-可以提供方便的制造商任务，专注于电子电路设计。 研究将开始通过建立一个语料库的互动体现在虚拟现实中，专家辅导员和学习者出现作为动画化身与现场跟踪，以驱动他们的运动和互动，以建立电子电路。第二阶段将在语料分析和数据的基础上建立一个原型教学代理。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。在较长的时间尺度上，发展的经验为基础的目录的促进动作有可能告知教师教育和教学代理人可以更好地支持远程学习。 该方法可扩展到依赖于具体交互的一系列任务，例如技能培训或物理治疗。该提案将对学习和计算机科学做出重大贡献。由于制造者促进往往依赖于教师的直觉决定，这不是一个很好理解的过程。语料库将提供重要的数据，口头和非口头的策略，以促进制造商教育。 对语料库和后来的主体-学习者互动的分析将提高我们对促进、非语言交流的作用以及在这些环境中如何指导学习的理解。 建立代理的技术工作将建立在这一领域的教学代理的效用。 该语料库还将提供一个独特而丰富的数据集，用于开发未来的行为合成机器学习算法。 该数据集将包括语言和非语言行为，沿着环境和交互对象的完整编码以及主持人应用的教学动作，超过目前可用的任何数据集。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.