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

这个i-Corps项目的更广泛的影响/商业潜力是开发旨在增强学生主动学习的人工智能(AI)解决方案。这一技术平台旨在提供对整个学年学生表现的动态评估。人工智能技术通过提供对困难学生以及有特殊才能的学生的早期检测，触发了及时的干预。与传统平台不同，该解决方案使用学生在课堂上的行为、作业成绩和常规测试分数等因素的组合来评估风险级别，并建议通用和个性化的反馈以及确定的提高学生学习表现的常规。该平台还将定期与学生/家长/教师交流学生的进步。拟议的技术可能会通过采取一种方法来增强学习体验，该方法排除了当前系统因新冠肺炎事件而浮出水面的缺陷。这个i-Corps项目基于开发一个AI(人工智能)解决方案，该解决方案使用先进的分析来提高学生的学习表现。这项拟议的技术结合了计算机视觉、深度学习、机器学习和自然语言处理等人工智能工具，可以彻底分析学生在课堂内外的行为。它提供重要的规范性分析，并使用推荐系统和协作过滤来提供动态反馈，并确定提高学生学习成绩的成功例程。该项目基于几项行为数据科学研究和高级分析在教育中产生数据驱动洞察的能力。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 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.