I-Corps: Software that predicts which undergraduates are at risk of dropping out or requiring more than four years to graduate

I-Corps：预测哪些本科生有退学风险或需要四年以上才能毕业的软件

• 批准号: 2226797
• 负责人: Mohsen Dorodchi
• 金额: $ 5万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226797&HistoricalAwards=false
• 关键词: Corps; Software; predicts; undergraduates; risk

The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence (AI)-based technology to enhance the understanding of college student success and how to help students align their financial resources and academic plans. The proposed technology advances techniques in data engineering, machine learning, and visualization, and may enable colleges and universities to leverage their massive student academic and financial records to support students’ success in more sophisticated, targeted capacities. The proposed technology may help higher education institutions achieve strategic goals for increasing graduation rates, improving time-to-degree completion, and eliminating equity gaps in graduation outcomes for low-income students. Students using the proposed technology may benefit from insights gained into the runway of eligibility for federal financial aid, allowing students to make more informed and efficient degree planning choices, graduate before exhausting lifetime aid eligibility, and limit their educational expenses and debt by reducing their overall time to degree. More individuals may earn baccalaureate degrees in less time and with less overall expense and student debt, and be ready to contribute better to society.This I-Corps project is based on the development of an intelligent system that incorporates financial, academic, and demographic data to accurately predict whether undergraduates are likely to graduate from 4-year institutions and if they will graduate within four years or more. The proposed technology has been designed to identify students who are at increased risk in terms of their financial health, including students on a trajectory to exhaust lifetime-limited federal financial aid resources before graduating. In addition, the developed scripts for data cleaning and feature engineering may be easily changed to fit a given university’s data models. Undergraduates may be able to use the proposed advanced web and mobile technologies to wisely plan their academic coursework in order to graduate before their financial aid runs out, resulting in improved academic outcomes. A full stack model of development characteristics includes software with layers developed based on the business requirements and processes while complying with flexible technology capable of working alone or integrating with existing enterprise software, secure integration of machine learning algorithms through microservices, and the ability to scale to large and small school sizes and integrate with different enterprise information systems.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.

这个I-Corps项目更广泛的影响/商业潜力是开发一种基于人工智能（AI）的技术，以提高对大学生成功的理解，以及如何帮助学生调整他们的财务资源和学术计划。拟议的技术推进了数据工程、机器学习和可视化技术，并可能使高校能够利用其大量的学生学术和财务记录来支持学生在更复杂、更有针对性的能力方面取得成功。拟议的技术可以帮助高等教育机构实现提高毕业率，改善完成学位的时间以及消除低收入学生毕业结果中的公平差距的战略目标。使用拟议技术的学生可能会受益于对联邦财政援助资格的深入了解，使学生能够做出更明智和更有效的学位规划选择，在用尽终身援助资格之前毕业，并通过减少他们的整体时间来限制他们的教育费用和债务。更多的人可以在更短的时间内获得学士学位，并减少总费用和学生债务，并准备为社会做出更好的贡献。这个I-Corps项目是基于一个智能系统的开发，该系统结合了财务，学术和人口统计数据，以准确预测本科生是否有可能从四年制院校毕业，以及他们是否会在四年或更长时间内毕业。这项拟议中的技术旨在识别那些在财务健康方面面临更大风险的学生，包括那些在毕业前耗尽终身有限的联邦财政援助资源的学生。此外，开发的数据清理和特征工程脚本可以很容易地改变，以适应给定的大学的数据模型。本科生可以使用拟议的先进网络和移动的技术来明智地规划他们的学术课程，以便在他们的经济援助用完之前毕业，从而提高学术成果。开发特征的全栈模型包括具有基于业务需求和流程开发的层的软件，同时遵守能够单独工作或与现有企业软件集成的灵活技术，通过微服务安全集成机器学习算法，以及扩展到大型和小型学校规模并与不同企业信息系统集成的能力。该奖项反映了NSF的法定基金会的使命是履行其使命，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价，被认为值得支持。