I-Corps: New image processing programs and data modeling algorithms for education environments

I-Corps项目更广泛的影响/商业潜力是开发旨在增强学生主动学习的人工智能解决方案。该技术平台旨在为整个学年的学生表现提供动态评估。人工智能技术通过早期发现困难学生和有特殊才能的学生，从而触发及时的干预。与传统平台不同，该解决方案结合了学生在课堂上的行为、家庭作业成绩和常规考试成绩等因素来评估风险水平，并建议普遍和个性化的反馈以及确定的常规方法来提高学生的学习成绩。该平台还将定期与学生/家长/老师沟通学生的进步情况。该技术可以排除因新冠肺炎疫情而暴露出来的现有系统的缺陷，从而提高学习经验。I-Corps项目的基础是开发AI（人工智能）解决方案，该解决方案使用先进的分析技术来提高学生的学习成绩。该技术将计算机视觉、深度学习、机器学习和自然语言处理等人工智能工具结合起来，彻底分析学生在课堂内外的行为。它提供了重要的规定性分析，并使用推荐系统和协同过滤来提供动态反馈，并确定了提高学生学习绩效的成功例程。该项目基于几项行为数据科学研究和高级分析的力量，可以在教育中生成数据驱动的见解。该奖项反映了美国国家科学基金会的法定使命，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 I-Corps project is the development of an AI (artificial intelligence) solution that is aimed at enhancing student active learning. This technology platform is aimed at providing dynamic assessments of student performance throughout the academic year. The AI technology triggers timely interventions by providing early detection of struggling students as well as students with special talents. Unlike traditional platforms, this solution uses a combination of factors such as students’ behavior in the classroom, homework grades ,and regular test scores to evaluate risk levels and recommends generalized and personalized feedback plus identified routines for improving student learning performance. The platform also will communicate students’ progress to students/parents/teachers regularly. The proposed technology may enhance the learning experience by taking an approach that excludes the flaws of current system surfaced by the COVID-19 pandemic.This I-Corps project is based on the development of an AI (artificial intelligence) solution that uses advanced analytics to enhance students' learning performance. The proposed technology uses a a combination of AI tools such as computer vision, deep learning, machine learning, and natural language processing to thoroughly analyze students’ behavior inside and outside the classroom. It provides important prescriptive analytics and uses recommendation systems and collaborative filtering to provide dynamic feedback and identifies successful routines for improving the student learning performance. This project is based on several behavior data science studies and the power of advanced analytics for generating data-driven insights in education.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.