Developing Students’ Systems Thinking and Data Analytics Skills in Civil and Environmental Engineering

Manhattan University, Bronx NY

Investigators

Abstract

This project aims to serve the national interest by creating active learning experiences that will help students develop systems thinking and data analytics skills in civil and environmental engineering. Students will learn how to analyze civil engineering systems to support data-driven decision making. These skills will enable students to design, build, operate, monitor, and maintain large complex civil infrastructure systems. A project-based learning approach can help students learn system design and analysis skills by applying engineering methods taught in class to real world problems. This project will develop seven educational modules that include a mini project, a dataset for a civil engineering system, assessment questions, and multimedia content in which example problems are solved. The effectiveness of the modules in improving students’ systems thinking and data analytics skills will be assessed in the context of multiple existing civil engineering courses. The results will be used to inform the development and implementation of the modules. Interactive workshops will be offered for civil engineering faculty to disseminate project results, train faculty in the use of the modules, and obtain feedback that will help guide the development and usage of the modules. Faculty who are interested in adopting the modules will be able to download them from online repositories. This project will help prepare students for the complex systems problems that they will encounter in the engineering workforce. The goal of this project is to improve student learning in civil engineering by introducing cross-cutting themes of systems thinking and data analytics in existing courses. This project will develop seven educational modules for the cross-cutting themes: (1) systems thinking; (2) modeling; (3) cost, economics, and long-range planning; (4) optimization and decision making; (5) machine learning; (6) network analysis; and (7) data collection. Each module will include instructional materials describing how to adapt the module to courses, a presentation to help instructors teach the techniques used in the module, examples showing how to solve related problems, questions for assessing student learning outcomes, a dataset, and a mini project so that students can apply the techniques they have learned. This project will assess the impact on students by addressing a set of research questions including: (1) To what extent does student learning of systems/analytics increase after use of the modules? (2) To what extent can students apply analytics techniques in different civil infrastructure planning, operations, and management contexts? (3) Have students’ mindset/attitude about the importance of systems thinking and analytics changed as a result of the modules? This study will collect assessment data for a control group that does not use the modules and a treatment group that uses the modules to determine differences due to the modules. To disseminate the results and obtain feedback from multiple stakeholders, including industry and the engineering education community, two workshops will be convened. The workshops will be run as charrettes, an interactive form of participant engagement that solicits feedback and reflection for the co-creation of knowledge. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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.