EXP: Collaborative Research: PerSketchTivity- Empowering and Inspiring Creative, Competent, Communicative, and Effective Engineers through Perspective Sketching

EXP：协作研究：PerSketchTivity - 通过透视草图赋予和启发有创造力、有能力、善于沟通和高效的工程师

• 批准号: 1441291
• 负责人: Julie Linsey
• 金额: $ 18万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1441291&HistoricalAwards=false
• 关键词: EXP; Collaborative; Research; PerSketchTivity; Empowering

The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by building examples and studying their possibilities for fostering learning as well as challenges to using them well. This project examines whether technology can support learning to freehand sketch. Sketching has been demonstrated to play an important role in a number of domains, including engineering, and the ability to quickly sketch has been shown to improve creativity by making it easier for engineers to generate ideas and communicate them. This project will modify artificial intelligence tools that support recognizing sketches to directly help teach undergraduate engineers how to sketch well. Research studies will examine whether the tool helps students learn sketching skills, and importantly how it influences their spatial reasoning ability. Thus, if successful this research will not only create tools to allow people to learn to sketch better, but also will advance our understanding of how spatial reasoning and sketching are linked, and could eventually lead to more effective engineering education.The project proposes two interconnected strands of work: developing the software tool and conducting research studies in the context of undergraduate engineering courses. The software tool will use a heterogenous set of classifiers to help provide feedback to learners as they perform a sequence of sketching exercises on tablets. The design process will iterate on the tool to explore what types of feedback are most helpful and how different classifiers can be used to detect different levels of sketching skill. The program of research will include studying whether sketching training leads to advances in spatial reasoning skills, whether it affects design self-efficacy and attitudes towards sketching, transfer of spatial skillsets to design activities in other courses, and how sketching skills correlate to success on spatial reasoning tasks. In addition, through iterative development including user-centered design processes, design principles for sketching based tools will be derived. Data sources will include both qualitative and quantitative data such as pre- and post-test spatial reasoning tasks, structured interviews, surveys, and artifact analysis. Additionally, students (N=approximately 30-40) using the new tool in class will be compared to control cohorts of approximately 30 students who either use traditional engineering curricula (little free-hand sketching and some isometric drawing) and a sketching curriculum without the AI tool.

网络学习和未来学习技术计划资助的努力将有助于设想下一代学习技术，并增进我们对人们如何在技术丰富的环境中学习的了解。网络学习探索 (EXP) 项目通过构建示例并研究其促进学习的可能性以及充分利用它们的挑战，探索新型学习技术的可行性。该项目研究技术是否可以支持徒手素描的学习。草图已被证明在包括工程在内的许多领域发挥着重要作用，并且快速绘制草图的能力已被证明可以通过使工程师更轻松地产生想法并进行交流来提高创造力。该项目将修改支持识别草图的人工智能工具，以直接帮助本科生工程师如何画好草图。研究将检验该工具是否有助于学生学习素描技能，以及重要的是它如何影响他们的空间推理能力。因此，如果这项研究成功，不仅会创造出让人们更好地学习素描的工具，而且还将增进我们对空间推理和素描如何联系起来的理解，并最终可能带来更有效的工程教育。该项目提出了两个相互关联的项目工作内容：开发软件工具并在本科工程课程的背景下进行研究。该软件工具将使用一组异构分类器来帮助学习者在平板电脑上进行一系列素描练习时提供反馈。设计过程将迭代该工具，以探索哪些类型的反馈最有帮助，以及如何使用不同的分类器来检测不同水平的素描技能。该研究计划将包括研究素描训练是否会导致空间推理技能的进步，是否会影响设计自我效能和对素描的态度，将空间技能转移到其他课程的设计活动中，以及素描技能如何与空间成功相关联。推理任务。 此外，通过包括以用户为中心的设计流程的迭代开发，将导出基于草图的工具的设计原则。数据源将包括定性和定量数据，例如测试前和测试后的空间推理任务、结构化访谈、调查和工件分析。此外，在课堂上使用新工具的学生（N = 约 30-40 名）将与约 30 名学生组成的对照组进行比较，这些学生要么使用传统工程课程（少量手绘草图和一些等轴测图），要么使用不使用新工具的草图课程。人工智能工具。