SBIR Phase II: A K-12 Goal-Setting and Reflection Platform that Builds Student Learning Skills and Mindset

小企业创新研究(SBIR)第二阶段项目的更广泛的影响/商业潜力可能是通过培养元认知和社会情感学习(SEL)技能，从根本上改变学生，特别是来自低收入和弱势群体的学生的学业和生活结果。该项目将推进该公司的技术，以学生为主导，并与学术内容和常规相结合的方式，建立关键的SEL和元认知技能。这一努力以一种可衡量的方式扩大了教师能力。该项目将把自然语言处理(NLP)技术应用于该公司专有的学生反馈和教师反馈数据，将训练机器学习(ML)驱动的预测算法来衡量反思质量，并将构建一个学习策略推荐引擎。这一创新将使教师和学生能够扩展他们的工具包，以有效地进行教学健全的学习策略。创新的更深层次应用释放了商业化潜力，并为学生学习带来了变革性的成果。小企业创新研究第二阶段项目将建立在该公司NSF资助的研究和第一阶段结果的基础上，这些研究和结果支持应用基于公司专有数据的NLP技术和ML算法的可行性和商业化，以“阅读”学生的想法，对他们进行质量评分，并帮助教师提供反馈。该项目的第一阶段将是一个工作量较低、价值较快的目标，即应用数据科学和分析来汇总实时质量评估，以衡量学生的成长。该项目将根据学生的反思质量和课堂环境，向学校行政人员提供反思质量的报告，并向教师提供反馈提示。第二阶段将是一项更高的努力。其中一个目标是建立一个由NLP和ML技术驱动的策略推荐引擎，并基于教育学和学习科学的最新研究。这项技术将分析过去的反思，以确定学生尝试了哪些学习策略，然后为教师提供建议，他们可以推荐教学驱动的策略，以改善学生的学习。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 broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase II project may be a fundamental shift in academic and life outcomes for students, especially those from low-income and vulnerable populations, by building metacognition and Social Emotional Learning (SEL) skills. This project will advance the company's technology that builds critical SEL and metacognition skills in a way that is student-led and integrated with academic content and routines. The effort expands teacher capacity in a measurable way. The project will apply Natural Language Processing (NLP) techniques to the company’s proprietary data of student reflections and teacher feedback, will train Machine Learning (ML) driven predictive algorithms to measure reflection quality and will build a learning strategies recommendation engine. This innovation will allow teachers and students to expand their toolkits of effective pedagogically-sound learning strategies. The deeper application of the innovation unlocks commercialization potential coupled with transformational outcomes for student learning.The Small Business Innovation Research Phase II project will build on the company's NSF-funded research and Phase I results that support the feasibility and commercialization of applying NLP techniques and ML algorithms based on the company’s proprietary data to “read” student reflections, rate them on a quality rubric, and help teachers provide feedback. Stage I of this project will be a lower effort, fast value objective to apply data science and analytics to roll-up real-time quality assessments to measure growth at the student. The project will provide reports on the reflection quality to school administrators and feedback tips to teachers based on student reflection quality and classroom context. Stage II would be a higher effort. One objective will be to build a Strategy Recommendation Engine driven by NLP and ML techniques and based on the latest research in pedagogy and learning sciences. This technology will analyze past reflections to identify which learning strategies a student has tried, then provide teachers with suggestions on pedagogy-driven strategies they can be recommended to improve the students' learning.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.