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

这个i-Corps项目的更广泛的影响/商业潜力是开发基于人工智能(AI)的技术，以增强对大学生成功的理解，以及如何帮助学生协调他们的经济资源和学业计划。这项拟议的技术推动了数据工程、机器学习和可视化技术的发展，并可能使学院和大学能够利用其海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。拟议中的技术可能有助于高等教育机构实现提高毕业率、缩短完成学位的时间以及消除低收入学生毕业结果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获得联邦经济援助资格的洞察，使学生能够做出更明智、更有效的学位规划选择，在用尽终身资助资格之前毕业，并通过减少他们获得学位的总体时间来限制他们的教育费用和债务。更多的人可能会在更短的时间内获得学士学位，总体费用和学生债务更少，并准备好为社会做出更好的贡献。这个i-Corps项目基于一个智能系统的开发，该系统整合了金融、学术和人口数据，以准确预测本科生是否可能从四年制院校毕业，以及他们是否将在四年或更长时间内毕业。这项拟议中的技术旨在识别在经济健康方面面临更大风险的学生，包括在毕业前耗尽终身有限的联邦经济援助资源的学生。此外，为数据清理和功能工程开发的脚本可以很容易地进行更改，以适应给定大学的数据模型。本科生也许能够使用拟议的先进网络和移动技术来明智地计划他们的学术课程，以便在他们的助学金用完之前毕业，从而提高学业成绩。全堆栈开发特征模型包括：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能够扩展到大小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 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.