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: 2201641
• 负责人: Abiola Akanmu
• 金额: $ 216.31万
• 依托单位: Virginia Polytechnic Institute and 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=2201641&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的理论知识，但使用这些理论来解决现实生活中的问题可能会遇到挑战。这一挑战导致了毕业生进入劳动力市场的准备工作与行业需求之间的不平衡。这种脱节在建筑业持续存在，因为建筑从业者继续强调技能短缺，这导致了低绩效和低生产率。该研究项目旨在将学习者与实践社区联系起来，从而使他们能够以专家的方式了解、思考、推理和解决现实生活中的问题。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，现场访问、嘉宾讲座和Capstone项目的导师)。随着学习者与实践社区的互动，这有可能为学习者感知专业的方式和发展他们自己的专业身份提供信息。这两个现象将通过定性研究加以检验。该项目旨在创建一个名为ConPEC的协作网络，以调查建筑行业从业者与教师的可及性如何影响建筑工程专业学生的纪律认知和职业认同发展。ConPEC平台将使用机器学习算法和复杂的数据分析，以便将教师与他们的实践社区配对。为了实现这一目标，本研究将首先调查建筑工程教师的实际课程支持需求和行业从业者的特征。接下来，ConPEC框架将被设计为包括学习驱动的算法，以利用教师和行业从业者之间的动态匹配模式。最后，该团队将使用与学生和行业从业者的半结构化访谈来收集定性数据，这些数据将使用扎根理论进行分析。该研究小组将研究学生纪律意识和职业认同发展的变化。该项目由NSF的EHR核心研究(ECR)项目支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。该计划支持积累强有力的证据，为理解、构建理论以解释并建议干预和创新以应对教育中持续存在的挑战的努力提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。