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

这个小型企业创新研究(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层次的关系，帮助学校识别缺乏牢固关系和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力量。学生情绪健康、学生缺勤和教师职业倦怠是当今K-12教育面临的一些最紧迫的问题。大量研究表明，积极的师生关系有助于学生适应学校，有助于社交技能的发展，提高学习成绩和适应能力，减少旷课，培养敬业精神。学校很难大规模地建立关系--建立联系需要时间，并不是所有的学生都愿意敞开心扉，教师需要帮助和培训，以理解和回应学生的不同经历和需求。这个项目如果成功，将帮助学校大规模地应对这些挑战。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张蓝图，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和系统。该项目构建了一种创新技术，将从规模上理解和衡量师生关系的力量。这项技术将开发新的框架，根据学生反思的深度、教师的回应以及回应如何一周又一周的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通常专注于使用模型来理解文本输入和预测/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。这两个模型的输出将结合在一起，通过创建学生-教师关系信任度量来了解学生-教师关系的强度。这一指标将有助于了解全国各地学校和地区的师生关系。这一奖项反映了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 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.