SBIR Phase II: Building Mathematical Thinking and Problem-solving Skills Together Through Play

SBIR 第二阶段：通过游戏共同培养数学思维和解决问题的能力

• 批准号: 2200238
• 负责人: Elizabeth Schwartz
• 金额: $ 100万
• 依托单位: mathbrix llc
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200238&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Building; Mathematical

This SBIR Phase I project will develop a machine learning-assisted family math preparation program that will help and encourage parents to provide early math instruction to young children, ages 4-8. Along with reading, developing mathematical proficiency is arguably one of the most important skills for young children to acquire, particularly with the growing importance of STEM (science, technology, engineering, and math) in the world today. Yet standardized test scores in math remain alarmingly low, with just 40% of US fourth grade students performing at grade level. A considerable body of research now shows that these gaps often appear before a child first enters kindergarten. Strong parental engagement in a child's mathematics learning is often cited as a key indicator of future academic success, but ways to bring about this engagement remain elusive. Grounded both in computational social science and constructivist models of learning, this project aims to develop a scaleable means of targeting parental engagement and promoting positive math learning experiences for families, goals which are fundamentally aligned with NSF's mission to promote the progress of science and advance national welfare, and which could have significant social and economic impact for years to come. This project will introduce several innovations, particularly in the areas of artificial intelligence and data modeling. A mobile learning application will be developed that provides users with a range of on- and off-screen math activities for parent and child to engage in together. Data will be collected from short surveys repeated at regular intervals and these will be collated together with information provided by the app (learning data, time-on-task, etc.). Deep learning approaches will be leveraged to explore relationships between content, engagement, and learning measures and to suggest follow-on activities predicted to improve these outcomes. While the data set will be limited during Phase I research, the technology will be able to identify an increasing number of connections as participation becomes more and more robust over time. In Phase II, these and other instruments will describe and detect family engagement practices at more than just one point in time, with differing socio-economic groups, and in a variety of different geographies.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SBIR I阶段项目将开发一个机器学习辅助的家庭数学准备计划，该计划将有助于并鼓励父母向4-8岁的幼儿提供早期的数学教学。除了阅读外，发展数学水平可以说是幼儿获得的最重要技能之一，尤其是随着当今世界上STEM（科学，技术，工程和数学）的重要性。然而，数学中的标准化考试成绩仍然令人震惊，只有40％的美国四年级学生在成绩上表现。现在，大量的研究表明，这些差距通常出现在孩子首次进入幼儿园之前。在儿童的数学学习中的强烈父母参与通常被认为是未来学术成就的关键指标，但是实现这种参与的方法仍然难以捉摸。该项目以计算社会科学和建构主义的学习模式为基础，旨在开发针对父母参与并促进家庭的积极数学学习经验的可扩展方式，这些目标与NSF从根本上保持一致，该目标与NSF促进科学进步和促进国家福利的使命，并在未来几年内可能会对社会和经济产生重大的社会和经济影响。该项目将引入几项创新，特别是在人工智能和数据建模领域。将开发一个移动学习应用程序，为用户提供一系列在屏幕上和屏幕外的数学活动，供父母和孩子一起参与。数据将从定期重复的简短调查中收集，并将这些数据与应用程序提供的信息（学习数据，任务中的时间等）一起整理。深度学习方法将被利用，以探索内容，参与度和学习措施之间的关系，并建议预测改善这些结果的后续活动。尽管在I阶段研究期间，数据集将受到限制，但随着参与的时间越来越强大，该技术将能够确定越来越多的连接。在第二阶段，这些和其他工具将在不仅仅一个时间点上描述和检测家庭参与实践，具有不同的社会经济群体，以及各种不同的地理位置。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来支持的。