SBIR Phase I: Sown To Grow - Measuring Growth in Trusting Relationships between Students and Educators with Natural Language Processing and Machine Learning Technologies

SBIR 第一阶段：播种成长 - 使用自然语言处理和机器学习技术衡量学生和教育工作者之间信任关系的增长

• 批准号: 2322340
• 负责人: Disha Gupta
• 金额: $ 27.5万
• 依托单位: SOWN TO GROW, INC.
• 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: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322340&HistoricalAwards=false
• 关键词: SBIR; Phase; Sown; Grow; Measuring

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project seeks to help educators to develop deeper relationships with their students, assist schools in identifying students who lack strong relationships and need additional support, and help school districts understand the emotional health and relationship strength of their schools. Student emotional well-being, student absenteeism, and teacher burnout are some of the most pressing problems facing K-12 education today. A significant body of research shows that positive student-teacher relationships help students adjust to school, contribute to social skill development, promote academic performance and resiliency, decrease absenteeism, and foster engagement. Schools struggle with relationship building at scale - it takes time to form connections, not all students are willing to open up, and teachers need help and training on understanding and responding to the varied experiences and needs of their students. This project, if successful, will help schools address these challenges at scale. Additionally, the data from this project will help teachers contribute to learning science and behavioral health research, while providing a blueprint to the education technology industry on how to implement advanced technology in an ethical and transparent manner that augments, rather than replaces, existing education structures and systems.This project builds an innovative technology that will understand and measure the strength of the student-teacher relationships at scale. The technology will develop new frameworks for defining trusting relationships based on the depth of student reflections, teacher responses, and how responses change and grow week over week. Advanced natural language processing (NLP) and machine learning (ML) techniques will model these frameworks based on real student-teacher interactions. NLP typically focuses on using models to understand text inputs and predict/generate responses. Through this project, the team seeks to use new NLP/ML techniques to understand and assess the interactions and levels of trust between individuals. The NLP/ML models will analyze the depth of student reflections and interpret the nature of the teacher responses separately. The output of these two models will then be combined to understand the strength of student-teacher relationship by creating a student-teacher relationship trust metric. This metric will help understand student-teacher relationships at scale across schools and districts all over the country.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响旨在帮助教育工作者与学生建立更深入的关系，帮助学校识别缺乏牢固关系并需要额外支持的学生，并帮助学区了解情感学校的健康状况和关系强度。学生情绪健康、学生缺勤和教师倦怠是当今 K-12 教育面临的最紧迫问题。大量研究表明，积极的师生关系有助于学生适应学校、促进社交技能发展、提高学业成绩和适应能力、减少缺勤并促进参与。学校在大规模建立关系方面遇到了困难——建立联系需要时间，并非所有学生都愿意敞开心扉，教师需要帮助和培训来理解和应对学生的不同经历和需求。该项目如果成功，将帮助学校大规模应对这些挑战。此外，该项目的数据将帮助教师为学习科学和行为健康研究做出贡献，同时为教育技术行业提供一个蓝图，说明如何以道德和透明的方式实施先进技术，以增强而不是取代现有的教育结构该项目构建了一种创新技术，可以大规模地理解和衡量师生关系的强度。该技术将开发新的框架，根据学生反思的深度、教师的反应以及反应如何每周变化和增长来定义信任关系。先进的自然语言处理（NLP）和机器学习（ML）技术将基于真实的师生互动对这些框架进行建模。 NLP 通常侧重于使用模型来理解文本输入并预测/生成响应。通过这个项目，该团队寻求使用新的 NLP/ML 技术来理解和评估个人之间的互动和信任水平。 NLP/ML 模型将分析学生反思的深度并分别解释教师反应的性质。然后将这两个模型的输出结合起来，通过创建师生关系信任指标来了解师生关系的强度。该指标将有助于了解全国各地学校和学区的师生关系。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。