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

EAGER：通过多模态机器学习协调数学学生之间的富有成效的协作

• 批准号: 2331379
• 负责人: Anthony Botelho
• 金额: $ 29.96万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331379&HistoricalAwards=false
• 关键词: EAGER; Orchestrating; Productive; Collaboration; Among

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.

该项目通过开发名为 MathCollaborate 的机器学习驱动技术创新，满足了对更有效的以教师为导向的协作学习支持工具的需求，几乎每个美国学生都需要学习代数 1，但 40% 的学生确实这样做了。甚至没有达到 2019 年国家教育进步评估所衡量的最低水平；而且有迹象表明，在服务不足群体的学生中，成绩水平甚至更低。学生参与丰富的数学活动和讨论的协作方式是当务之急，但对许多数学教师来说也是一个挑战。支持 MathCollaborate 开发和研究的项目活动将通过为参与教师提供有关学生数学的见解来帮助解决这些挑战。在线和面对面的表现、参与和讨论，使数学教学更加集中。该项目将有助于更广泛地确定在课堂环境中使用协作活动的有效教学和教学法。该项目旨在支持数学教师和学生。学生参与协作数学活动和讨论利用人工智能和机器学习进行在线协作数学学习，该项目的智力价值符合三个总体目标，首先，该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。其次，项目团队将探索学生小组互动和讨论数学内容时出现的协作范式的类型，其中我们将利用多个数据源来研究学生在协作过程中表现出的互动和话语。活动以及这些活动与学习成果的关系。最后，项目团队将利用他们在该项目中学到的知识，结合多模式机器学习方法来构建高效和非高效协作策略的检测器。该团队将把这些检测器开发成 MathCollaborate 的功能原型，并检查其支持教师的能力。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。