Improving Students’ Modeling Skills for Engineering Complex Systems

该项目旨在通过帮助学生学习必要的技能来管理协作虚拟环境中复杂工程系统的整个生命周期，从而为国家利益服务。基于模型的系统工程（MBSE）是一种新兴的方法，它解决了在包括硬件和软件组件的复杂系统的工程中提高生产力、提高质量、降低风险和改善通信的需求。这个项目将帮助学生学习如何使用MBSE在系统开发周期的每个阶段，包括需求，设计，分析，验证和确认，通过开发一个新的课程，使用基于项目的学习。学生将在地理分布的团队中工作，其中包括来自德克萨斯大学格兰德河谷和德克萨斯大学埃尔帕索分校的成员，以学习如何在虚拟协作环境中工作。将开发一种机器学习算法来衡量学生在课程期间的情绪，以确定及时改进课程教学方式的机会。项目成果将通过年度ASEE会议、系统工程会议、IEEE期刊出版物和一个开源网站向工程教育界传播，该网站提供MBSE培训材料、手册以及机器学习算法的设计和开发指南。该项目的目标是提高STEM员工在系统工程方面的技术技能，重点是MBSE。该项目将追求四个相辅相成的目标。首先是为工业和系统工程，制造工程和工程技术的第三和第四年的学生开发MBSE本科课程。第二是为地理上分散的学生团队提供协作的自组织动态团队体验。第三是开发一种机器学习技术，以分析和理解学生对在地理上分离的学生团队中使用MBSE的看法。第四是创建一个开源平台，与有兴趣教授MBSE的学术机构分享项目材料和见解。用于分析在线技术论坛和团队交流的文本数据的机器学习模型将有助于描述学生的学习体验。学生的学习将在布鲁姆分类法的多个层次上进行评估，包括：（1）学生是否能够使用系统建模语言符号创建工程系统的系统图和模型？(2)学生是否能够使用MBSE生成系统需求、建模架构并定义规范？(3)学生是否能够根据他们生成的系统模型生成系统验证和测试计划？教师将对模型和文件进行评估，以确定学生在多大程度上取得了学习成果。该项目预计将推进有关在协作虚拟环境中工作的知识，并使用机器学习来评估学生的看法。IUSE计划通过其“学生学习”（Student Learning）项目，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知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by helping students learn the necessary skills to manage the full lifecycle for complex engineering systems in collaborative virtual environments. Model-based Systems Engineering (MBSE) is an emerging methodology that addresses the need to increase productivity, improve quality, reduce risk, and improve communications in the engineering of complex systems that include both hardware and software components. This project will help students learn how to use MBSE in each phase of the system development cycle including requirements, design, analysis, verification, and validation, by developing a new course using project-based learning. Students will work in geographically distributed teams that will include members from the University of Texas Rio Grande Valley and the University of Texas at El Paso to learn how to work in a virtual collaborative environment. A machine learning algorithm will be developed to measure student sentiment during the course to identify opportunities for improving how the course is taught in a timely manner. Project results will be disseminated to the engineering education community through the annual ASEE conference, systems engineering conferences, IEEE journal publications, and an open-source website with information on MBSE training materials, manuals developed, and a design and development guide of machine learning algorithms.The goal of this project is to improve the technical skills of the STEM workforce in systems engineering with a focus on MBSE. The project will pursue four complementary objectives. First is to develop an undergraduate MBSE course for third and fourth year students in industrial and systems engineering, manufacturing engineering, and engineering technology. Second is to provide a collaborative self-organizing dynamic team experience for geographically separated student teams. Third is to develop a machine learning technique to analyze and understand student perceptions of using MBSE in geographically separated student teams. Fourth is to create an open-source platform to share project materials and insights with academic institutions that are interested in teaching MBSE. The machine learning model for the analysis of text data from online technical discussion forums and team communications in the course will help characterize students’ learning experience. Student learning will be assessed at multiple levels of Bloom’s Taxonomy including: (1) Are students able to create system diagrams and models of engineered systems using Systems Modeling Language notations? (2) Are students able to generate system requirements, model architectures, and define specifications using MBSE? (3) Are students able to generate system verification and test plans based on system models they generate? The models and documents will be assessed by instructors to determine to what extent the students have achieved the learning outcomes. This project is expected to advance knowledge about working in collaborative virtual environments and the use of machine learning to assess students’ perceptions. Through its Engaged Student Learning track, the IUSE 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.