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

利用机器学习检查工科学生在创业教育项目中的自我选择

• 批准号: 2321175
• 负责人: Prateek Shekhar
• 金额: $ 35万
• 依托单位: New Jersey Institute of Technology
• 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: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321175&HistoricalAwards=false
• 关键词: Leveraging; Machine; Learning; Examine; Engineering

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.

该项目旨在通过增进本科工科学生参与创业教育项目的理解来服务国家利益。创业和创新对于经济成功非常重要，而在当今高科技世界中，工程师往往在引领创新方面发挥着核心作用。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来发现机会并了解市场和业务需求。创业教育项目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计划的优势，确保更多的工程专业学生群体接触到创业计划变得非常重要。然而，学生自主选择创业教育项目的情况较多，这方面的研究还比较缺乏。该项目通过调查研究问题来检验工程学生的自我选择：工程学生的人口、社会经济和学术背景如何预测他们对工程创业项目的参与？通过对学生参与创业教育项目建立基于研究的理解，这个个人研究者发展项目通过为外展、招聘和项目工作提供见解来服务于国家利益，从而扩大这些项目在本科工程教育中的影响。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和学术背景如何交互式地预测他们在创业教育项目中的入学（或不入学）。随着 STEM 教育界不断开发创新的教育干预措施，调查哪些学生至关重要正在参加此类计划，特别是从人口的角度来看。该项目利用强调学生背景重要性的社会选择理论，其目标是利用回归和机器学习技术作为探索性、数据驱动的方法来检查工程专业学生对创业教育项目的参与情况。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交互主义观点来研究社会选择，该观点考察学生人口统计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来促进概念理解，为新兴工程创业教育领域的未来研究奠定基础。此外，该项目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。该项目得到了比尔及梅琳达·盖茨基金会、施密特期货公司和沃尔顿家族基金会的合作支持。该项目还得到了 NSF 的 EDU STEM 教育研究核心研究能力建设（ECR：BCSER）计划的支持，该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 领域的参与以及 STEM 劳动力发展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。