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

这个项目的目标是开发CueLearn，一个教育网络应用程序，将促进有效的同行合作，以提高中学生的数学问题解决能力。研究表明，当学生参与搭建良好的、共享的和协作的学习体验时，学习往往会更好、更持久。然而，教育技术并不经常利用这项研究，最常关注的是个人而不是群体。因此，CueLearn项目将开发、测试和实施两个智能策略，以改善学生的协作问题解决能力：1)通过在CueLearn内创建有效的同行小组来促进有效的学生协作；2)使用实时支持来增加学生的参与度，并帮助学生有效地坚持下去。该项目的主要目标是设计一个系统，该系统将轻松促进所有学生的高质量同伴协作，同时还支持教师使用技术进行协作。该项目通过自动分配学生与优化的同伴小组一起工作，专注于社交学习体验。将通过一系列观察性研究和实验来测试多种分组策略，以确定哪种分组策略效果最好。例如，学生可以根据他们想要的问题解决策略进行分组，这样学生就可以接触到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对于自己的学习可能并不有效，除非学生在解决问题的过程中也得到额外的支持。因此，将使用机器学习方法来监测参与度，并实时检测无效形式的持久性，以便在需要时提供即时支持。重要的是，这些支持将与学生共同设计。假设自动理想分组和即时支持相结合，以提高学生的问题解决成绩和数学信念。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有效的教育技术。探索研究预科-12项目(DRK-12)旨在通过研究和开发创新资源、模型和工具，显著提高预科-12学生和教师在科学、技术、工程和数学(STEM)方面的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发工作的基础上，为拟议的项目提供了理论和经验上的证明。该奖项反映了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。

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.