Using Computational Modeling to Transform Assessments of Creativity in Engineering Design

使用计算建模转变工程设计中的创造力评估

• 批准号: 2155071
• 负责人: Dan Johnson
• 金额: $ 6.33万
• 依托单位: Washington and Lee University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2155071&HistoricalAwards=false
• 关键词: Using; Computational; Modeling; Transform; Assessments

This collaborative project from research teams at Pennsylvania State University, University of Maryland, and Washington and Lee University focuses on measuring creativity in undergraduate engineering education. The ability to think creatively is essential for success in STEM fields, particularly engineering, which requires designing solutions to complex problems that often have no single or "correct" solution. The Next Generation Science Standards identify creative thinking skills, such as problem solving and flexibility, as core competencies for modern STEM education. Yet educators are not currently equipped with adequate tools to assess creativity in their classrooms. To effectively prepare the STEM workforce, there is a critical need for assessment tools that educators and researchers can use to identify what works in STEM education to foster creativity. Current creativity tests present significant challenges for STEM educators, including (in-person) paper administration and, perhaps most problematically, manual scoring that requires teachers to count and code thousands of responses—a labor-intensive and often costly process, particularly for under-resourced schools. In light of the increasingly diverse student population, the availability of creativity tests that measure student ability fairly and consistently, regardless of race or ethnicity, is even more critical for equity of opportunity in STEM education. This project seeks to create an online platform for measuring creativity in engineering design that educators can use to cater to the needs of all their students. The tool will allow educators to administer a range of engineering creativity tasks and automatically calculate creativity scores. This project fits the intent of the ECR program to facilitate "the development, refinement, and testing of new education research, measurement, and evaluation methodologies." It addresses the ECR research track, "Research on STEM Learning and Learning Environments," and has additional impacts for "Research on Broadening Participation in STEM Fields" by designing inclusive and culturally and linguistically diverse assessment tools targeted to students who remain underrepresented in the pursuit of STEM courses of study and English as second language speakers.Two aims guide this project. First is to build an online platform for large-scale engineering design assessment — validating all platform tasks with undergraduate engineering students — to allow teachers and researchers to easily assess creativity, automatically compute creativity metrics, and generate customizable student reports. Second is to apply the platform in an undergraduate design course at Penn State that includes a 3-week Creativity Module (with lessons and exercises on creativity in engineering design) to obtain valuable platform usability data from both instructors and students, while evaluating a promising undergraduate course intended to promote creativity in engineering design. The team will apply recent advances in computational modeling and machine learning — including active learning of design sketches and distributional semantic modeling of text-based responses to creative problem solving tasks. It is expected that this approach will streamline educational assessment of creativity, resulting in a user-friendly technology to assist STEM educators in the classroom. The novel computational tools developed in this project will advance knowledge and understanding for creativity psychometric assessment and across different fields (not only engineering). The PI team will also design assessment tools that are culturally responsive and minimally biased — especially for the growing number of students who speak English as a second language — and collaborate with STEM educators to maximize the usability of the platform in their classrooms. The online platform and course materials will be publicly available, facilitating the national transition to remote education and research (accelerated by the current pandemic) by providing online resources for STEM teachers and researchers across the country.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.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.

这个合作项目来自宾夕法尼亚州立大学、马里兰州大学和华盛顿和李大学的研究团队，重点是测量本科工程教育中的创造力。创造性思维的能力对于STEM领域的成功至关重要，特别是工程领域，这需要为复杂的问题设计解决方案，这些问题通常没有单一或“正确”的解决方案。下一代科学标准将创造性思维技能，如解决问题和灵活性，确定为现代STEM教育的核心能力。然而，教育工作者目前还没有配备足够的工具来评估课堂上的创造力。为了有效地准备STEM劳动力，迫切需要评估工具，教育工作者和研究人员可以使用这些工具来确定STEM教育中的哪些工作可以促进创造力。目前的创造力测试对STEM教育工作者提出了重大挑战，包括（亲自）纸张管理，也许最有问题的是，需要教师计算和编码数千个响应的手动评分-这是一个劳动密集型且往往昂贵的过程，特别是对于资源不足的学校。鉴于学生群体的日益多样化，公平和一致地衡量学生能力的创造力测试的可用性，无论种族或民族如何，对于STEM教育的机会公平更为重要。该项目旨在创建一个在线平台，用于衡量工程设计中的创造力，教育工作者可以使用该平台来满足所有学生的需求。该工具将允许教育工作者管理一系列工程创造力任务，并自动计算创造力分数。该项目符合ECR计划的意图，以促进“新的教育研究，测量和评估方法的开发，改进和测试。“它涉及ECR研究轨道，“STEM学习和学习环境研究”，并通过设计包容性和文化和语言多样性的评估工具，针对那些在追求STEM学习课程和英语作为第二语言的学生，对“扩大STEM领域参与的研究”产生额外的影响。两个目标指导这个项目。首先是构建一个用于大规模工程设计评估的在线平台-与本科工程学生一起验证所有平台任务-使教师和研究人员能够轻松评估创造力、自动计算创造力指标并生成可定制的学生报告。其次是在宾夕法尼亚州立大学的本科设计课程中应用该平台，其中包括为期3周的创意模块（包括工程设计创意的课程和练习），以从教师和学生那里获得有价值的平台可用性数据，同时评估旨在促进工程设计创意的有前途的本科课程。该团队将应用计算建模和机器学习的最新进展-包括设计草图的主动学习和基于文本的分布式语义建模，以解决创造性问题。预计这种方法将简化创造力的教育评估，从而产生一种用户友好的技术，以帮助STEM教育工作者在课堂上。在这个项目中开发的新的计算工具将推进知识和理解的创造力心理测量评估和跨不同领域（不仅工程）。PI团队还将设计具有文化响应性和最小偏见的评估工具-特别是针对越来越多的以英语为第二语言的学生-并与STEM教育工作者合作，最大限度地提高平台在课堂上的可用性。在线平台和课程材料将公开提供，通过为全国的STEM教师和研究人员提供在线资源，促进全国向远程教育和研究的过渡（因当前的疫情而加速）。该项目得到NSF的EHR核心研究（ECR）计划的支持。ECR计划强调基础STEM教育研究，产生该领域的基础知识。投资是在关键领域是必不可少的，广泛的和持久的：干学习和干学习环境，扩大参与干，干劳动力发展。该计划支持积累强有力的证据，为理解、建立理论解释、提出干预和创新建议以应对教育中持续存在的挑战提供信息。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。