Collaborative Research: Connecting Professional and Educational Communities to Prepare Construction Engineering Students for the Workplace

合作研究：连接专业和教育社区，为建筑工程专业的学生做好进入工作场所的准备

• 批准号: 2201642
• 负责人: Andrea Ofori-Boadu
• 金额: $ 20.14万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201642&HistoricalAwards=false
• 关键词: Collaborative; Research; Connecting; Professional; Educational

While universities equip students with theoretical knowledge for STEM, there can be challenges in employing those theories to solve real-life problems. This challenge has resulted in an imbalance between the preparation of graduates entering the workforce and the demands of the industry. This disconnection is persistent in the construction industry as construction practitioners continue to highlight skill shortages that have resulted in low performance and low productivity. This research project is designed to connect learners with communities of practice thereby giving them access to expert ways of knowing, thinking, reasoning, and solving real-life problems. The research team will develop a tool that connects construction engineering programs with communities of practice thereby enabling instructors access to industry practitioners with the appropriate expertise to meet their practical course-support needs (e.g., site visits, guest lectures, and mentors for capstone projects). As learners interact with communities of practice, this has the potential to inform the ways learners perceive the profession and the development of their own professional identity. These two phenomena will be examined through a qualitative study. This project is designed to create a collaborative network (called ConPEC) to investigate how the accessibility of construction industry practitioners to instructors, influences construction engineering students’ disciplined perception and professional identity development. The ConPEC platform will employ machine learning algorithms and complex data analysis to allow for pairing instructors with their community of practice. To achieve this, the research will first investigate the practical course-support needs of construction engineering instructors and the characteristics of industry practitioners. Next, the ConPEC framework will be designed to include learning-driven algorithms to exploit dynamic matching patterns between instructors and industry practitioners. Finally, the team will use semi-structured interviews with both students and industry practitioners to gather qualitative data that will be analyzed using Grounded Theory. The research team will examine changes in students’ disciplined perception and professional identity development.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.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.

虽然大学为学生提供 STEM 理论知识，但运用这些理论解决现实生活问题可能存在挑战。这一挑战导致了毕业生进入劳动力市场的准备与行业需求之间的不平衡。这种脱节在建筑行业持续存在，因为建筑从业者不断强调技能短缺导致绩效低下和生产率低下。该研究项目旨在将学习者与实践社区联系起来，从而使他们能够获得了解、思考、推理和解决现实生活问题的专家方法。研究团队将开发一种工具，将建筑工程项目与实践社区联系起来，从而使教师能够接触到具有适当专业知识的行业从业者，以满足他们的实际课程支持需求（例如，现场参观、客座讲座和顶点项目的导师）。 当学习者与实践社区互动时，这有可能告诉学习者如何看待职业和发展他们自己的职业身份。 这两种现象将通过定性研究进行检验。 该项目旨在创建一个协作网络（称为 ConPEC），以调查建筑行业从业者与讲师的接触程度如何影响建筑工程学生的纪律观念和职业身份发展。 ConPEC 平台将采用机器学习算法和复杂的数据分析，以便将讲师与其实践社区配对。 为了实现这一目标，研究将首先调查建筑工程讲师的实际课程支持需求和行业从业者的特征。 接下来，ConPEC 框架将被设计为包含学习驱动的算法，以利用教师和行业从业者之间的动态匹配模式。最后，该团队将使用对学生和行业从业者的半结构化访谈来收集定性数据，并使用扎根理论进行分析。 研究团队将研究学生纪律观念和职业认同发展的变化。该项目得到了 NSF 的 EHR 核心研究 (ECR) 计划的支持。 ECR 项目强调基础 STEM 教育研究，产生该领域的基础知识。投资针对重要、广泛和持久的关键领域：STEM 学习和 STEM 学习环境、扩大 STEM 参与以及 STEM 劳动力发展。该计划支持积累强有力的证据，为理解、建立解释理论以及提出干预和创新建议以解决教育中持续存在的挑战提供信息。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。