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

该项目旨在通过增进对工科本科生参与创业教育项目的理解，服务于国家利益。企业家精神和创新精神对经济成功至关重要，在当今高科技世界，工程师往往在引领创新方面发挥核心作用。为了在全球技术创新经济中成功竞争，毕业工程师需要拥有发现机会和了解市场和商业需求的创业技能。创业教育方案继续被认为是培养工科和其他STEM毕业生创业技能和创新能力的一种机制。随着越来越多的证据支持创业规划的优势，确保更广泛的工科学生群体接触创业规划是很重要的。然而，学生往往自我选择进入创业教育项目，这方面的研究还很缺乏。该项目通过调查以下研究问题来考察工科学生的自我选择：工科学生的人口统计、社会经济和学术背景如何预测他们参与工程创业项目？通过建立以研究为基础的对学生参与创业教育项目的理解，这一个人调查员发展项目为拓展、招聘和项目努力提供见解，以扩大这些项目在本科工程教育中的影响，从而服务于国家利益。该项目使用创新的量化方法来研究工科学生的人口统计和学术背景如何交互地预测他们在创业教育项目中的入学人数(或未入学人数)。随着STEM教育社区继续开发创新的教育干预措施，调查哪些学生正在参加此类项目是至关重要的，特别是从人口统计学的角度。借鉴强调学生背景重要性的社会选择理论，本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、数据驱动的方法来审查工科学生参与创业教育项目的情况。由于背景因素可能以复杂的方式相互关联，该项目的重点是使用互动主义观点来审查社会选择，该观点考察了学生人口统计因素的动态相互作用。该项目通过提供数据驱动的模型来解释学生的参与，为未来在新兴的工程创业教育领域的研究奠定基础，从而促进了对概念的理解。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。该项目通过与比尔和梅琳达·盖茨基金会、施密特期货公司和沃尔顿家庭基金会的合作伙伴关系得到支持。该项目还得到了NSF的STEM教育研究EDU核心研究能力建设计划(ECR：BCSER)的支持，该计划旨在建设研究人员在STEM学习和学习环境、扩大STEM领域的参与和STEM劳动力发展等核心领域开展高质量STEM教育研究的能力。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。

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.