Leveraging Machine Learning to Examine Engineering Students Self-selection in Entrepreneurship Education Programs

该项目旨在通过促进对本科工程专业学生参与创业教育计划的理解来服务于国家利益。企业家精神和创新对经济成功至关重要，而工程师在当今高科技世界的创新中往往发挥着核心作用。为了在全球技术创新经济中成功竞争，即将毕业的工程师需要具备创业技能，以识别机会并了解市场和业务需求。创业教育计划继续被认为是培养工程和其他STEM毕业生创业技能和创新能力的机制。随着越来越多的证据支持创业编程的优势，重要的是要确保更广泛的工程专业学生群体接触创业编程。然而，学生往往自我选择进入创业教育项目，并在这方面的研究缺乏。该项目通过调查研究问题来研究工程专业学生的自我选择：工程专业学生的人口统计学，社会经济和学术背景如何预测他们参与工程创业计划？通过建立一个基于研究的理解学生参与创业教育计划，这个个人研究者发展项目服务于国家利益，提供了深入的宣传，招聘和方案的努力，扩大这些计划在本科工程教育的影响。该项目使用创新的定量方法来研究工程专业学生的人口统计学和学术背景如何交互预测他们在创业教育项目中的入学（或未入学）。随着STEM教育社区不断开发创新的教育干预措施，调查哪些学生正在入学这类项目至关重要，特别是从人口统计学的角度。利用社会选择理论，强调学生背景的重要性，该项目的目标是利用回归和机器学习技术作为一种探索性的，数据驱动的方法来检查工程专业学生参与创业教育计划。由于背景因素可能以复杂的方式相互关联，因此该项目的重点是使用互动主义观点来研究社会选择，该观点研究了学生人口因素的动态相互作用。该项目通过提供解释学生参与的数据驱动模型，为工程创业教育这一新兴领域的未来研究奠定了基础，从而有助于推进概念理解。此外，该项目还将研究基于回归的方法和先进的机器学习建模技术（及其算法变体）的有效性和适用性，这可以促进类似方法在工程教育研究中的使用。这个项目是通过与比尔梅林达盖茨基金会，施密特期货，沃尔顿家族基金会的合作伙伴关系支持。该项目也得到了NSF的EDU核心研究STEM教育研究能力建设的支持（ECR：BCSER）计划，旨在培养研究人员在STEM学习和学习环境的核心领域开展高质量STEM教育研究的能力，扩大STEM领域的参与，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。

This project aims to serve the national interest by advancing the understanding of undergraduate engineering students' participation in entrepreneurship education programs. Entrepreneurship and innovation are important for economic success, and engineers often hold a central role in leading innovation in today's high-technology world. To compete successfully in the global technological innovation economy, graduating engineers need to possess entrepreneurial skills to identify opportunities and understand market and business needs. Entrepreneurship education programs continue to be recognized as a mechanism for developing entrepreneurial skills and innovativeness in engineering and other STEM graduates. With increasing evidence supporting the advantages of entrepreneurship programming, it is important to ensure that broader engineering student populations are exposed to entrepreneurship programming. However, students often self-select into entrepreneurship education programs, and there is a lack of research in this regard. The project examines engineering students' self-selection by investigating the research question: How do engineering students' demographic, socio-economic, and academic backgrounds predict their participation in engineering entrepreneurship programs? By building a research-based understanding of student participation in entrepreneurship education programs, this individual investigator development project serves the national interest by providing insights for outreach, recruitment, and programmatic efforts, to widen the impact of these programs in undergraduate engineering education. The project uses innovative quantitative methods to examine how engineering students' demographic and academic background interactively predict their enrollment (or non-enrollment) in entrepreneurship education programs.As the STEM education community continues to develop innovative educational interventions, it is critical to investigate which students are enrolling in such programs, particularly from a demographic standpoint. Drawing on social selection theory that highlights the importance of students' backgrounds, the goal of this project is to leverage regression and machine learning techniques as an exploratory, data-driven approach to examine engineering students' engagement in entrepreneurship education programs. Because background factors are likely to be associated with each other in complex ways, the focus of the project is to examine social selection using an interactionist view which examines the dynamic interplay of student demographic factors. The project contributes to advancing conceptual understanding by providing data-driven models explaining student participation that lay the foundation for future research in the emerging field of engineering entrepreneurship education. In addition, the project will study the effectiveness and suitability of regression-based methods and advanced machine learning modeling techniques (and their algorithmic variants), which can advance the use of similar approaches in engineering education research. This project is supported through a partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. This project is also supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research in the core areas of STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development.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.