Collaborative Research: Introducing High-School Students to Computational Thinking in Industrial Automation

随着计算已成为科学，技术，工程和数学（STEM）实践的组成部分，STEM+计算计划旨在通过在幼儿教育中应用计算思维和计算活动的整合来解决计算STEM领域的新挑战。强大的制造业对国家的经济繁荣至关重要。制造业的工人越来越需要具备使用计算思维、软件和先进技术的技能。该项目将开发一系列数字发明工具包，使高中生能够重建这些发明的数字对应物的工作模型。例如，达文波特旋转电机，专利于1837年，将被用作脚手架的步进电机套件，结合了数字微控制器的发展。学生将因此了解原始的模拟机电机制导致今天的数字同行的发展方式。 拟议的数字发明工具包将（1）提供计算思维的介绍，（2）招募传统上不被STEM领域吸引的多样化学生，（3）向这些学生介绍工业自动化。通过这些项目开发的相关基础设施将提高学校在K-12创客空间提供更广泛的制造技术的能力。更广泛的学生参与这些项目也将导致社区学院及其他地区先进制造项目的多样性增加。完成的数字发明工具包和配套的教学材料，评估项目和教师支持材料将在Make to Learn网站上提供，并由专业组织传播。该项目还将举办讲习班和短期课程，向技术和工程教育工作者介绍这些材料。（1）数字发明套件在提高计算思维方面的有效性，（2）在整个套件序列中，套件在吸引和留住女性和代表性不足的学生方面的有效性，以及（3）课程中引入的计算思维策略如何影响学生的设计和产品开发。该项目将使用混合方法来评估参与者在计算思维的各个方面的增长，同时使用定量和定性方法。为了检查学生的思维过程和解决方案，该项目将使用定性的方法来研究计算策略，行动，编码和产品，沿着与理论和相关的信念。这些将包括对学生的观察，工件分析，基于任务的面试，以STEM为重点的面试和教师评估（包括测试和最终课程成绩）。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。

As computing has become integral to the practice of science, technology, engineering and mathematics (STEM), the STEM+Computing program seeks to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within STEM teaching and learning in early childhood education through high school (preK-12). A strong manufacturing industry is essential for the nation's economic prosperity. Workers in manufacturing increasingly need to have skills involving the use of computational thinking, software and advanced technologies. The project will develop a series of Digital Invention Kits that will enable high school students to reconstruct working models of the digital counterparts of these inventions. For example, the Davenport Rotary Motor, patented in 1837, will be used as scaffolding for development of a Stepper Motor Kit that incorporates a digital micro-controller. Students will thereby learn about the way in which the original analog electromechanical mechanisms led to development of today's digital counterparts. The proposed Digital Invention Kits will (1) provide an introduction to computational thinking, (2) recruit diverse students who are not traditionally attracted to STEM fields, and (3) introduce these students to industrial automation. The associated infrastructure developed through these projects will increase the capacity of schools to offer a broader range of manufacturing technologies in K-12 makerspaces. Participation in these programs by a broader range of students will also lead to increased diversity in advanced manufacturing programs in community colleges and beyond. The completed Digital Invention Kits and accompanying pedagogical materials, assessment items, and teacher support materials will be made available on the Make to Learn website and disseminated by professional organizations. The project will also conduct workshops and short courses introducing these materials to technology and engineering educators.The project will investigate: (1) how effective the Digital Invention Kits in improving computational thinking, (2) how effective the Kits are in engaging and retaining female and underrepresented students for the entire Kit sequence, and (3) how the computational thinking strategies introduced in the program affect student design and product development. The project will use a mixed method approach to assess participant growth in various aspects of computational thinking, using both quantitative and qualitative methods. To examine student thought processes and solutions, the project will use qualitative approaches to study computational strategies, actions, coding and products, along with rationales and related beliefs. These will include observations of students, artifact analyses, task-based interviews, STEM-focused interviews, and instructor assessments (including tests and final course grades. Research outcomes will be presented at STEM conferences and will be published in peer-reviewed journals.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.