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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛/商业影响旨在帮助教育者与学生建立更深层的关系，帮助学校确定缺乏牢固关系并需要额外支持的学生，并帮助学区了解学校的情感健康和关系的力量。当今K-12教育面临的一些最紧迫的问题，学生的情感幸福感，学生缺勤和教师倦怠是最紧迫的问题。大量的研究表明，积极的学生教师关系可以帮助学生适应学校，为社会技能发展做出贡献，促进学习成绩和韧性，降低缺勤和促进参与。学校在大规模建立关系方面挣扎 - 需要时间才能建立联系，并非所有学生都愿意开放，并且老师需要帮助和培训，以理解和回应学生的各种经验和需求。如果成功的话，该项目将帮助学校大规模应对这些挑战。此外，该项目的数据将有助于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有关如何以道德和透明的方式实施先进技术的蓝图，以增强而不是代替现有的教育结构和系统。该项目将建立一个创新的技术，以理解和衡量学生会关系的力量在规模上。该技术将开发新的框架，以根据学生的思考，教师回应的深度以及一周一周的变化和增长方式来定义信任关系。先进的自然语言处理（NLP）和机器学习（ML）技术将基于实际的学生教师互动对这些框架进行建模。 NLP通常专注于使用模型来了解文本输入并预测/生成响应。通过该项目，团队试图使用新的NLP/ML技术来理解和评估个人之间信任的互动和水平。 NLP/ML模型将分析学生思考的深度，并分别解释教师回答的性质。然后，将组合这两个模型的输出，以通过建立学生教师关系信任度量来了解学生教师关系的实力。该指标将有助于了解全国各地各个学校的学生教师关系。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。