Collaborative Research: CueLearn: Enhancing Social Problem Solving through Intelligent Support

协作研究：CueLearn：通过智能支持增强社会问题解决能力

• 批准号: 2300827
• 负责人: Caitlin Mills
• 金额: $ 198.61万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300827&HistoricalAwards=false
• 关键词: Collaborative; Research; CueLearn; Enhancing; Social

The goal of this project is to develop CueLearn, an educational web application that will foster effective peer collaborations to improve mathematical problem solving amongst middle school students. Research suggests that learning is often better and longer lasting when students engage in well-scaffolded, shared, and collaborative learning experiences. However, educational technologies do not often leverage this research, with the focus most often being on the individual rather than a group. The CueLearn project will thus develop, test, and implement two intelligent strategies to improve students' collaborative problem solving: 1) facilitating effective student collaborations through the creation of effective peer groups within CueLearn, and 2) using real-time supports to increase student engagement and help students persist productively. The project's key aim is to design a system that will easily facilitate quality peer collaboration for all students, while also supporting teachers in their use of technology for collaborative work.The project focuses on social learning experiences by automatically assigning students to work with optimized peer groups. Multiple grouping strategies will be tested through a series of observational studies and experiments to identify which grouping strategy works best. For example, students may be grouped based on their intended problem-solving strategy, so that students will be exposed to diverse approaches from peers who are using different strategies. However, intelligent student grouping methods may not be effective for learning by themselves, unless students are also provided with additional support during problem solving. Machine learning approaches will therefore be used to monitor engagement and detect unproductive forms of persistence in real-time in order to provide in-the-moment support as needed. Critically, these supports will be co-designed with students. Automated ideal grouping combined with just-in-time supports are hypothesized to improve students' problem solving performance and mathematical beliefs. Through all of these design innovations, the project aims to provide a new and effective educational technology for students and teachers.The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.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.

这个项目的目标是开发CueLearn，一个教育网络应用程序，将促进有效的同伴合作，以提高中学生的数学问题解决能力。研究表明，当学生参与良好的脚手架，共享和协作学习经验时，学习往往更好，更持久。然而，教育技术并不经常利用这项研究，重点往往是个人，而不是一个群体。因此，CueLearn项目将开发、测试和实施两种智能策略，以提高学生的协作问题解决能力：1）通过在CueLearn中创建有效的同伴群体，促进有效的学生协作; 2）使用实时支持来提高学生参与度，帮助学生高效地坚持下去。该项目的主要目标是设计一个系统，方便所有学生进行高质量的同伴协作，同时支持教师使用技术进行协作工作。该项目通过自动分配学生与优化的同伴小组一起工作，重点关注社会学习体验。将通过一系列观察性研究和实验对多种分组策略进行测试，以确定哪种分组策略效果最好。例如，学生可以根据他们预期的解决问题的策略进行分组，这样学生就可以接触到来自使用不同策略的同龄人的不同方法。然而，智能的学生分组方法可能不会有效地学习自己，除非学生在解决问题的过程中也提供了额外的支持。因此，机器学习方法将用于实时监测参与并检测非生产性形式的持久性，以便根据需要提供即时支持。重要的是，这些支持将与学生共同设计。自动化的理想分组与即时支持相结合的假设，以提高学生的问题解决能力和数学信念。通过所有这些设计创新，该项目旨在为学生和教师提供一种新的和有效的教育技术。发现研究preK-12计划（DRK-12）旨在通过研究和开发创新资源，模型和工具，显着提高preK-12学生和教师的科学，技术，工程和数学（STEM）的学习和教学。DRK-12项目中的项目建立在STEM教育的基础研究以及为拟议项目提供理论和经验依据的先前研究和开发工作的基础上。该奖项反映了NSF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。