EAGER: Orchestrating Productive Collaboration Among Students in Mathematics with Multimodal Machine Learning

该项目通过开发一种名为MathCollaborate的机器学习驱动的技术创新，解决了对更有效的面向教师的协作学习支持工具的需求。几乎每个美国学生都被要求参加代数1，然而40%的学生甚至没有达到2019年国家教育进步评估所衡量的最低熟练程度；而且所有迹象表明，大流行使情况变得更糟。在服务不足群体的学生中，成绩水平甚至更低。为学生提供更多令人兴奋和协作的方式，让他们参与丰富的数学活动和讨论是当务之急，但对许多数学教师来说，这是一个挑战。支持MathCollaborate开发和研究的项目活动将通过为参与教师提供关于学生在线和面对面的数学表现、参与度和话语的见解，从而帮助应对这些挑战，从而实现更有针对性的数学教学。该项目将帮助在课堂环境中更广泛地使用协作活动来确定有效的教学和教学方法。该项目旨在通过利用人工智能和机器学习进行在线协作数学学习来支持数学教师和学生参与协作数学活动和讨论。这个项目的智力价值与三个首要目标保持一致。首先，这个项目将研究教师如何在课堂上利用和开展协作工作，并探索如何设计和实施技术来更好地支持教师的需求。其次，项目团队将探索在学生群体互动和讨论数学内容时出现的合作范例的类型。在这方面，我们将利用多个数据来源来研究学生在合作活动中表现出的互动和话语，以及这些与学习结果的关联。最后，项目团队将利用他们在这个项目中学到的东西，结合多模式机器学习方法来构建生产性和非生产性协作策略的检测器。该团队将把这些探测器开发成MathCollaborate的功能原型，并检查其支持教师在课堂上协调协作活动的能力。这一奖项反映了NSF的法定使命，并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。

This project addresses the need for more effective teacher-oriented support tools for collaborative learning through the development of a machine-learning powered technical innovation called MathCollaborate. Almost every student in the United States is required to take Algebra 1, yet 40% of students did not achieve even the lowest proficiency level measured by the National Assessment of Educational Progress in 2019; and there is every indication that the pandemic made the situation even worse. Among students of underserved groups, the achievement levels are even lower. Providing more exciting and collaborative ways for students to engage in rich mathematics activities and discussions is a priority, but a challenge for many math teachers. The project activities supporting the development and research of MathCollaborate will help address these challenges by providing participating teachers with insights about students' math performance, engagement, and discourse, both online and in-person, enabling more focused math instruction. This project will help identify effective instruction and pedagogy around the use of collaborative activities in classroom settings, more broadly.This project seeks to support math teachers and students as they engage in collaborative mathematics activities and discussions by leveraging artificial intelligence and machine learning for online collaborative math learning. The intellectual merit of this project aligns to three overarching goals. First, this project will examine how teachers utilize and conduct collaborative work in their classrooms and explore how technology could be designed and implemented to better support teachers’ needs. Second, the project team will explore the types of collaborative paradigms that emerge as groups of students interact and discuss mathematics content. Within this, we will leverage multiple data sources to study the interactions and discourse exhibited by students during collaborative activities as well as how these correlate with learning outcomes. Finally, the project team will utilize what they learn in this project in conjunction with multimodal machine-learning methods to build detectors of productive and unproductive collaboration strategies. The team will develop these detectors into a functional prototype of MathCollaborate and examine its ability to support teachers’ orchestration of collaborative activities in their classrooms.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.