Combining Machine Learning Explanation Methods with Expectancy-Value Theory to Identify Tailored Interventions for Engineering Student Persistence

将机器学习解释方法与期望值理论相结合，确定针对工程学生坚持的定制干预措施

• 批准号: 2335725
• 负责人: Campbell Bego
• 金额: $ 60.26万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-15 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335725&HistoricalAwards=false
• 关键词: Combining; Machine; Learning; Explanation; Methods

This project aims to serve the national interest by applying machine learning (ML) methods to improve engineering student persistence. The increasing demand for a diverse, qualified engineering workforce requires an improvement in undergraduate program persistence rates, especially for underrepresented and minoritized students. This Engaged Student Learning Level 2 project intends to apply well-established predictive ML methods and cutting-edge ML explanation methods to identify and predict which students might leave and why. The project team plans to identify tailored interventions to meet individual student needs. This project will test the viability and variability of various ML methods and generalize the methodology for other universities. The proposed project has the potential to generate knowledge and streamline a methodology for the identification of individualized interventions for engineering persistence. The project is grounded in expectancy-value theory. The proposed methodology will help engineering education researchers to begin intervening in the first year to help at-risk students persist. This project also has the potential to have broad impacts on computer science communities which require real-world applications of emerging tools. Project findings will be presented at the American Society of Engineering Education annual conference and Frontiers in Education annual conference. In addition, all python code for each stage will be made available through the Open Science Foundation. The codes will be accompanied with detailed instructions for using the proposed methodology with new data. The proposed methods to improve engineering persistence potentially have broad impacts in a local, nation-wide, or global scale. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.

该项目旨在通过应用机器学习（ML）方法来提高工程专业学生的持久性，从而为国家利益服务。对多元化，合格的工程劳动力的需求不断增加，需要提高本科课程的持续率，特别是对于代表性不足和少数民族学生。该项目旨在应用成熟的预测性ML方法和尖端的ML解释方法来识别和预测哪些学生可能会离开以及为什么离开。项目小组计划确定量身定制的干预措施，以满足个别学生的需求。该项目将测试各种ML方法的可行性和可变性，并将该方法推广到其他大学。拟议的项目有可能产生的知识和简化的方法来确定个性化的干预工程持久性。该项目以预期价值理论为基础。建议的方法将有助于工程教育研究人员开始干预的第一年，以帮助有风险的学生坚持。该项目也有可能对计算机科学社区产生广泛的影响，这些社区需要新兴工具的实际应用。项目成果将在美国工程教育学会年会和教育前沿年会上发表。此外，每个阶段的所有Python代码都将通过开放科学基金会提供。这些守则将附有详细说明，说明如何使用新数据的拟议方法。所提出的提高工程持久性的方法可能在地方、全国或全球范围内产生广泛的影响。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生STEM教育的有效性。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。