EAGER: Cyberlearning with Co-Robotic Teachable Agents

网络学习技术结合了强大的个性化支持和与合作机器人可教学代理的激励参与，为协作机器人STEM教育的发展和广泛传播提供了引人注目的高风险高回报机会。该项目将创建机器人可教学代理技术，提供个性化的社交，情感和认知支持，以提高学生的STEM学习和动机。该项目将吸引至少10名教师和250名中学生，包括许多代表性不足的少数民族中学生，参与协作机器人和STEM网络学习的研究。研究人员将让学生和教师参与ChalkTalk（一种基于手势的混合现实讲故事和模拟工具包）与中学几何机器人可教学代理（R-TAG）的设计和集成，以创建一个用于正式和非正式STEM学习的开源工具包。研究人员将进行实证研究，以开发丰富的学生认知和社会互动模型，并提高对如何使用物理和虚拟可教机器人代理设计个性化认知，社会和情感支持的理解。该项目将为学生和教师创造一个低成本的机会，参与协作机器人和STEM网络学习。该项目将有助于开发和测试教育战略，以扩大来自教育途径中代表性不足的群体的学生对机器人职业的参与。这项研究将推进最先进的合作机器人网络学习，让数百名代表性不足的少数民族中学生参与个性化的泛在网络学习与协作机器人体验（UCCRE）。该项目将（1）研究UCCRE如何通过协作性的体现个性化互动来开发感知和认知支持，从而产生探索性的洞察力，适应性反馈和自我解释-超越典型的辅导支持（提供提示、指导性解释）;（2）探讨UCCRE如何发展社会支持，通过与机器人可教学代理的具体个性化互动，吸引和激励学生，以及（3）通过一系列使用基于设计的研究方法的试点研究评估UCCRE，情感和生理测量，以及自然语言处理（NLP）工具。研究人员将使用这些方法、措施和工具来感知和适应学习者的情感状态，以促进学习和创造力。学生和教师将迭代推进UCCRE的能力，以提供个性化的自适应认知和社会支持，并促进机器人可教代理，学生和教师之间的合作体现互动，在正式的课堂和非正式的课后设置。普遍存在的合作机器人的参与式设计的研究结果可能会推广到各个领域，并提高学生学习机器人的兴趣和动力。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Cyberlearning technologies that incorporate robust personalized support and motivational engagement with co-robotic teachable agents present compelling high risk-high payoff opportunities for advancement and broad dissemination of collaborative-robotics STEM education. The project will create robotic teachable agent technologies that provide personalized social, affective, and cognitive support to improve students' STEM learning and motivation. The project will engage at least 10 teachers and 250 middle school students including many underrepresented minority middle school students in the study of collaborative robotics and STEM cyberlearning. The researchers will involve students and teachers in the design and integration of ChalkTalk (a Mixed Reality gesture-based storytelling and simulation tool-kit) with Robotic-Teachable Agents for middle school Geometry (R-TAG) to create an open-source tool-kit for formal and informal STEM learning. The researchers will conduct empirical studies to develop rich models of student cognitive and social interactions, and improve understanding of how to design personalized cognitive, social, and affective support using physical and virtual teachable robotic agents. The project will create a low-cost opportunity for students and teachers to engage in collaborative robotics and STEM cyberlearning. The project will contribute to development and testing of education strategies for broadening participation of students from groups underrepresented in education pathways to careers in robotics. This research will advance the state-of-the-art of co-robotic cyberlearning, engaging hundreds of underrepresented minority middle school students in personalized Ubiquitous Cyberlearning with Collaborative Robotic Experiences (UCCRE). The project will (1) investigate how UCCRE can develop perceptual and cognitive support through collaborative embodied personalized interaction that generates exploratory insight, adaptive feedback, and self-explanation - going beyond typical tutoring support (offering hints, instructional explanations); (2) investigate how UCCRE can develop social support, through embodied personalized interaction with robotic teachable agents that engage and motivate students and (3) evaluate UCCRE through a series of pilot studies using design-based research methods, affective and physiological measures, and Natural Language Processing (NLP) tools. The researchers will use these methods, measures, and tools to sense and adapt to learners' affective states in ways that promote learning and creativity. Students and teachers will iteratively advance the capacity of UCCRE to provide personalized adaptive cognitive and social support, and facilitate collaborative embodied interactions between robotic teachable agents, students and teachers, within formal classroom and informal after-school settings. The findings on participatory design of ubiquitous co-robotics will likely generalize across domains and improve students' interest and motivation in learning robotics.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.