SBIR Phase I: A K12 Goal-Setting And Reflection Platform That Builds Student Learning Skills And Mindset

这个SBIR第一阶段项目将开发一个人工智能（AI）/机器学习（ML）驱动的解决方案，以自动化对学生反思提供定性反馈的过程。自动阅读学生反思的质量和内容是一种新的创新技术，目前在市场上还没有，因为这个领域的所有应用程序都集中在语法和写作结构上。创新将了解学生正在使用的具体行动和策略（其中许多是独特的个人学习者），并帮助学生获得他们需要的支持，以提高这些技术或尝试新的技术。这一过程将在学生中建立学习技能和以成长为导向的信念体系，这两者都能显著改善学术和职业成果。从根本上说，拟议的项目将帮助学生学会如何学习。SBIR第一阶段项目将建立第一阶段的个性化见解，以实现有效的学生目标设定和反思。该公司将利用自然语言处理（NLP）和ML算法来1）解析，处理和分析学生撰写的反思，2）根据基于研究的规则返回每个反思的分数，以及3）当学生得分较低并需要支持时通知教师。在第一阶段结束时，该公司将检查原型是否能以90%以上的准确率按预期运行，教师是否能将原型整合到课堂实践中，以及它是否能提高学生反思和学习的质量。凭借40万+书面学生反思和数据点的专有数据集，该公司在开发和扩展这一产品愿景方面具有独特的优势。第二阶段的研究将开发个性化的反馈，策略和建议，以帮助学生和教师更快地找到最好的策略。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

This SBIR Phase I project will develop an Artificial Intelligence (AI) /Machine Learning (ML) driven solution to automate the process of giving qualitative feedback on student reflections. The automated reading of student reflections for quality and content is a new and innovative technology that is currently not available in the market, as all applications in this realm focus on grammar and writing structures. The innovation will understand the specific actions and strategies students are using (many of which are unique to an individual learner), and help students get the support they need to improve in those techniques or try new ones. This process would build learning skills and growth-oriented belief systems in students, both of which lead to significantly improved academic and career outcomes. Fundamentally, the proposed project will help students learn how to learn.This SBIR Phase I project will build the first phase of personalized insights for impactful student goal-setting and reflection. The company will leverage natural language processing (NLP) and ML algorithms to 1) parse, process, and analyze reflections written by students, 2) return a score for each reflection based on a research-based rubric, and 3) notify teachers when a student has a low score and needs support. At the end of Phase I, the company will examine whether the prototype functions as intended with 90%+ accuracy, if teachers can integrate the prototype into their classroom practice, and if it shows early promise of improving the quality of student reflections and learning. With a proprietary dataset of 400K+ written student reflections and data points, the company is uniquely positioned to develop and scale this product vision. Phase II research will develop personalized feedback, strategies and suggestions to help students and teachers find the best strategies faster.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.