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

创客教育具有振兴和多样化数学、科学和工程教育的重要前景，它结合了复杂技术实践的低门槛，丰富的社区基础设施资源和支持，以及有趣的学习心态。然而，创客教育最好是亲自完成的，学习者和促进者可以讨论正在建造的工件，每个人都可以操纵这些对象，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。这给创客教育的规模化带来了挑战，因为难以提供有效的远程教学，也难以创造教学资源。该提案将开发具体化的教学代理——虚拟环境中的动画角色——可以为创客任务提供便利，专注于电子电路设计。研究将从在具体化的虚拟现实中建立一个互动语料库开始，在这个语料库中，专家促进者和学习者以动画化身的形式出现，通过实时跟踪来驱动他们的动作，并通过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效益，并将加强大学与社区的伙伴关系。在更长的时间尺度上，基于经验的促进行动目录的发展有可能为教师教育提供信息，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互动的一系列任务，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。由于创客促进往往依赖于教师的直觉决策，这不是一个很好的理解过程。语料库将为促进创客教育的言语和非言语策略提供重要数据。对语料库的分析和随后的主体-学习者互动将提高我们对促进的理解，非语言交际的作用，以及如何在这些环境中指导学习。构建智能体的技术工作将确立教学智能体在该领域的实用性。该语料库还将提供一个独特而丰富的数据集，用于开发未来用于行为综合的机器学习算法。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，以及促进者应用的教学动作，超出目前可用的任何内容。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。

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.