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个工具来帮助学生在建筑工程和管理课程中学习计算思维技能，从而服务于国家利益。该项目侧重于主动学习体验，学生可以学习如何从大型数据集中提取有意义的信息，并利用结果做出明智的工程决策。这些经验可以帮助学生更好地满足建筑行业的需求，例如提高生产力、减少浪费和改善工人安全。在建筑工地上使用传感器是一种日益增长的趋势，因为它们提供实时数据来显示工地上发生的情况。学生需要培养数据分析和计算思维技能，以便能够处理传感器数据、执行数据分析并了解建筑工地操作。为了实现这些目标，项目团队将开发一个网络应用程序，为学生提供图形界面来选择、分析和显示传感器数据。学生将能够实时探索建筑工地，了解工地上物体之间的行为和关系，以及它们与建筑项目安全和生产力的关系。该网络应用软件将通过公共软件存储库提供给工程教育界。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。该项目的目标是通过让学生参与建筑工地的现实问题来提高学生的计算思维技能。 它将通过开发一个网络应用程序来实现这一目标，该应用程序为学生提供交互式可编程对象，以便在建筑工程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解决工程问题的重要计算概念的理解。学生将学习从建筑工地收集的传感器数据、数据分析的计算方法，以及理解低级传感器数据以支持决策。该应用程序将为网络浏览器开发，并将包括一个Python包，其中包含自定义对象（例如车辆、工人）和计算函数库。将进行混合方法研究，以回答以下研究问题：(1) 建筑行业所需的数据分析能力，(2) 帮助学生发展能力所需的 Web 应用程序元素，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范围的调查，以确定使用建筑工地传感系统所需的技能，并确定建筑行业对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 计划支持研究和开发项目，以提高所有学生 STEM 教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建、探索和实施。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。

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