Collaborative Research: Improving Students’ Computational Thinking Skills in Construction Engineering and Management

协作研究：提高学生在建筑工程和管理方面的计算思维能力

• 批准号: 2111003
• 负责人: Abiola Akanmu
• 金额: $ 47.73万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111003&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Students; Computational

This project aims to serve the national interest by creating a tool to help students learn computational thinking skills in construction engineering and management courses. The project focuses on active learning experiences in which students learn how to extract meaningful information from large datasets and use the results to make informed engineering decisions. These experiences can help better prepare students to address construction industry needs, such as increasing productivity, reducing waste, and improving worker safety. The use of sensors on construction sites is a growing trend because they provide real-time data showing what is happening on a site. Students need to develop skills in data analytics and computational thinking so that they can process sensor data, perform data analyses, and develop an understanding of construction site operations. To accomplish these aims, the project team will develop a web application that provides students with a graphical interface to select, analyze, and display sensor data. Students will be able to explore a construction site in real-time to understand behaviors and relationships between objects on a site and how they relate to construction project safety and productivity. The web application software will be made available to the engineering education community through public software repositories. By addressing the computational skills gap in the construction industry, this project will benefit construction workers and the economic competitiveness of construction companies.The goal of this project is to improve students’ computational thinking skills by engaging students in real world problems on construction sites. It will do so by developing a web application that provides interactive programmable objects for students to perform computational functions in the context of construction engineering. The application will help students analyze sensor data and improve their understanding of important computational concepts for engineering problem solving. Students will learn about sensor data collected from construction sites, computational approaches for data analyses, and making sense of low-level sensor data to support decision making. The application will be developed for web browsers and will include a Python package that contains custom objects (e.g., vehicles, workers) and a library of computational functions. A mixed-method research study will be conducted to answer research questions that address (1) competencies in data analytics that are needed in the construction industry, (2) the elements of the web application that are needed to help students develop the competencies, and (3) the impact of the web application on students’ computational thinking. A nationwide survey of construction engineering and management professionals will be conducted to identify skills that are needed for working with construction site sensing systems and to determine the value of and demand for these skills in the construction industry. The project will introduce high school students to computational thinking in the construction industry through an existing outreach activity that involves a construction project. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.

该项目旨在通过创建一种工具来帮助学生学习建筑工程和管理课程中的计算思维技能来服务国家利益。该项目着重于积极的学习经验，学生学习如何从大型数据集中提取有意义的信息并使用结果来做出明智的工程决策。这些经验可以帮助学生做好准备以满足建筑行业需求的准备，例如提高生产力，降低浪费和提高工人安全。在建筑工地上使用传感器是一个增长的趋势，因为它们提供了实时数据，显示了网站上正在发生的情况。学生需要开发数据分析和计算思维方面的技能，以便他们可以处理传感器数据，执行数据分析并发展对建筑工地运营的理解。为了实现这些目标，项目团队将开发一个Web应用程序，该应用程序为学生提供图形接口，以选择，分析和显示传感器数据。学生将能够实时探索建筑工地，以了解站点上对象之间的行为和关系，以及他们如何与建筑项目的安全性和生产力相关。 Web应用程序软件将通过公共软件存储库提供给工程教育社区。通过解决建筑行业的计算技能差距，该项目将使建筑工人和建筑公司的经济竞争力受益。该项目的目的是通过使学生在建筑工地上遇到现实世界中的问题来提高学生的计算思维技能。它将通过开发一个Web应用程序来实现，该应用程序为学生提供可编程对象，以便学生在施工工程的背景下执行计算功能。该应用程序将帮助学生分析传感器数据，并提高他们对工程问题解决重要计算概念的理解。学生将了解从施工站点收集的传感器数据，用于数据分析的计算方法，并了解低级传感器数据以支持决策。该应用程序将针对Web浏览器开发，并包括一个包含自定义对象（例如车辆，工人）和计算功能库的Python软件包。将进行一项混合方法研究，以回答（1）建筑行业所需的数据分析能力的研究问题，（2）（2）Web应用程序的元素可以帮助学生发展能力，以及（3）Web应用程序对学生计算思维的影响。将对建筑工程和管理专业人员进行全国性的调查，以确定与建筑工地敏感系统合作所需的技能，并确定建筑行业这些技能的价值和需求。该项目将通过涉及建筑项目的现有外展活动向高中生介绍建筑行业的计算思维。 NSF IUSE：EHR计划支持研发项目，以提高所有学生STEM教育的有效性。通过参与的学生学习轨道，该计划支持了承诺实践和工具的创建，探索和实施。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。

项目成果

Data Analytics and Computational Thinking Skills in Construction Engineering and Management Education: A Conceptual System

建筑工程与管理教育中的数据分析和计算思维技能：概念系统

• DOI: 10.1061/9780784483985.021
• 发表时间: 2022
• 期刊: Construction Research Congress 2022
• 作者: Akanmu, Abiola A.;Akligo, Vincent S.;Ogunseiju, Omobolanle R.;Lee, Sang Won;Murzi, Homero
• 通讯作者: Murzi, Homero