PFI-TT: Using Big Data Analytics to Empower K-12 Teachers for Instructional Improvement

这个创新-技术翻译合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是为全球教育开发一个协作学习和分析的在线平台。该技术使用教育大数据和机器学习技术的最新发展，使K-12教师能够进行教学改进。随着COVID-19大流行加速了教师和学生学习的数字化转型，人工智能发挥了关键作用，K-12学校系统已成为大数据的最新前沿。尽管社会需求巨大，但K-12系统仍然是技术改造最少的领域之一。教师目前缺乏能够有效支持他们的教案、反思和学习的技术工具。拟议的技术旨在满足这些需求，并可能产生经济价值，因为教育技术在促进推动经济增长所需的人力资本方面发挥着关键作用。这个由妇女和有色人种领导的团队将通过在为来自低收入家庭的大部分有色人种学生提供服务的学校中试行初始原型来扩大参与。拟议项目采用参与式设计为基础的实施方法，与教师共同设计，并为教师设计。拟议的技术将包括高质量的教学材料库，教师可以调整并纳入自己的课程计划。该平台利用机器学习分析，使教师能够有效地进行自我学习，并由其他教师指导和指导。此外，这项技术的开发将采用严格的准实验方法，结合定量和定性证据，以检查其在改善教学和学生成果方面的有效性，以及影响其在学校中的实用性的因素。该技术将由一个代表其持续成功所需的生态系统的团队提供支持，包括教育和数据科学研究，产品开发和技术转让以及营销和商业战略方面的专业知识。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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 this Partnerships for Innovation - Technology Translation (PFI-TT) project is to develop an online platform for collaborative learning and analytics for global education. The proposed technology uses the latest developments in educational big data and machine learning techniques to empower K-12 teachers for instructional improvement. K-12 school systems have become the latest frontier in big data, as the COVID-19 pandemic has accelerated the digital transformation of teacher and student learning with artificial intelligence playing a key role. Despite a great societal need, K-12 systems remain one of the areas least transformed by technology. Teachers currently lack the kind of technological tools that can efficiently support their lesson plan, reflection, and learning. The proposed technology aims to address these needs and may have potential to generate economic value, as education technology places a critical role in advancing the human capital needed to drive economic growth. This team, led by women and people of color, will broaden participation by piloting the initial prototype in schools that serve large proportions of students of color from low-income families. The proposed project uses a participatory design-based implementation approach to co-design with and for teachers. The proposed technology will include libraries of high-quality instructional materials that teachers can adapt and incorporate into their own lesson plans. The platform leverages machine learning analytics to allow teachers to efficiently conduct self-learning and be mentored and coached by other teachers. Moreover, the development of this technology will incorporate a rigorous, quasi-experimental approach that combines both quantitative and qualitative evidence to examine its effectiveness on improving teaching and student outcomes, as well as factors that moderate its usefulness in schools. The proposed technology will be supported by a team that represents the ecosystems necessary for its ongoing success, including expertise in education and data science research, product development and technology transfer, and marketing and business strategy.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.