Engineering Online Learning Pathways in Advanced Manufacturing and Data Science

先进制造和数据科学的工程在线学习途径

• 批准号: 1935674
• 负责人: Samuel Spiegel
• 金额: $ 199.98万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935674&HistoricalAwards=false
• 关键词: Engineering; Online; Learning; Pathways; Advanced

This project will contribute to the national need for well-educated engineers and technicians in production engineering. It will do so by supporting the design, deployment, and evaluation of modular, online data science courses for advanced manufacturing. Big data sets are now available to describe workflows, properties of materials, and manufacturing and automation processes used in advanced manufacturing. However, few manufacturing process engineers understand how to tap into this data, organize and analyze it, and use it to improve manufacturing processes. This project will develop online educational pathways of modules and courses to teach practicing engineers and other learners how to apply data science in the production manufacturing environment. The courses will be developed by the Colorado School of Mines, in collaboration with the Red Rocks Community College, Colorado Community College Online, and multiple industrial partners. Individuals from these organizations will form a convergent team with expertise in advanced manufacturing and data science, industry needs and perspectives, and educational design and learning science. This project has the potential to nurture and grow the advanced manufacturing workforce, as well as to enhance the infrastructure for online education and educational research.The project will use a convergence approach to course design by bringing together experts from university, community college, and industry, who have broad disciplinary expertise and skills in data science, advanced manufacturing, and engineering education. The resulting courses will focus on data science tools, data science methodology, foundations of advanced manufacturing, applying data science to advanced manufacturing, and ways of learning and working in the modern era. The courses will be organized into multiple educational pathways designed to serve individual learners, as well as learners in industries, two-year colleges, and four-year colleges and universities. The primary objectives of the project are to: 1. Create pathways that encourage and support diverse learners to become proficient in using data science to solve advanced manufacturing problems; 2. Use an Engineering Learning Framework developed by the Principal Investigator, to design, develop, deploy, evaluate, and disseminate sell-assessment tools, modules, and courses aligned to form learning pathways that empower diverse learners to reskill or enhance their skills to tackle advanced manufacturing problems through data science. 3. Conduct project evaluation that will guide instructional design of the courses and modules. 4. Engage industry partners and two-year institutions to provide guidance on workforce needs as well as to pilot assessments, modules, and courses. 5. Complete a research study to explore how psychological and demographic characteristics affect learner performance. Project outcomes will be presented at national conferences and the materials will be freely available on the project website.This project is funded by NSF's EHR Core Research: Production Engineering Education and Research (ECR: PEER) program, which seeks to improve the education of future and current professionals in production engineering. It also aims to study how effective the innovative educational strategies adopted by these projects are in producing their desired objectives.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.

该项目将有助于满足国家对生产工程方面受过良好教育的工程师和技术人员的需求。 它将通过支持先进制造业模块化在线数据科学课程的设计、部署和评估来实现这一目标。 大数据集现在可用于描述先进制造中使用的工作流程、材料属性以及制造和自动化流程。 然而，很少有制造工艺工程师了解如何利用这些数据，组织和分析它，并使用它来改进制造工艺。该项目将开发模块和课程的在线教育途径，以教授实践工程师和其他学习者如何在生产制造环境中应用数据科学。这些课程将由科罗拉多矿业学院与红岩社区学院、科罗拉多社区学院在线和多个工业合作伙伴合作开发。 来自这些组织的个人将组成一个融合的团队，拥有先进制造和数据科学，行业需求和观点以及教育设计和学习科学方面的专业知识。 该项目将采用融合的方法，将来自大学、社区学院和工业界的专家汇聚在一起，他们在数据科学、先进制造和工程教育方面拥有广泛的学科专业知识和技能。 由此产生的课程将侧重于数据科学工具，数据科学方法，先进制造的基础，将数据科学应用于先进制造，以及现代学习和工作的方式。 这些课程将分为多个教育途径，旨在为个人学习者以及行业，两年制学院和四年制学院和大学的学习者提供服务。 该项目的主要目标是：1。 创建途径，鼓励和支持不同的学习者熟练使用数据科学来解决先进的制造问题; 2. 使用由首席研究员开发的工程学习框架，设计，开发，部署，评估和传播自我评估工具，模块和课程，以形成学习途径，使不同的学习者能够重新掌握或提高他们的技能，通过数据科学解决先进的制造问题。 3. 进行项目评估，指导课程和模块的教学设计。 4. 让行业合作伙伴和两年制机构参与进来，就劳动力需求提供指导，并进行试点评估、模块和课程。5. 完成一项研究，探索心理和人口统计特征如何影响学习者的表现。 项目成果将在全国性会议上公布，相关材料将在项目网站上免费提供。该项目由NSF的EHR核心研究：生产工程教育和研究（ECR：PEER）计划资助，旨在改善未来和现有生产工程专业人员的教育。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。

项目成果

Development of Self-Efficacy and Mindset Scales for Advanced Manufacturing and Data Sciences

先进制造和数据科学的自我效能感和心态量表的开发

• 发表时间: 2022
• 期刊: ASEE annual conference exposition
• 作者: Zappe, Sarah;Cutler, Stephanie;Spiegel, Samuel A.;Blacklock, Jennifer;Jordan, Deborah;Garcia, Francisco
• 通讯作者: Garcia, Francisco