RET Site: Biologically Inspired Computing Models, Systems, and Applications

该奖项更新了圣母大学教师研究经验网站。该网站将在圣母大学计算机科学专业的学生和教师、圣母大学职前教育项目的学生以及大南本德地区的中小学教师之间建立伙伴关系。在夏季，教师参与者将从事研究项目，重点关注与机器学习(ML) /人工智能（AI）算法相关的进步、能力和挑战，这些算法目前正在推动技术、制造业和政府部门的努力。为了将他们的研究经验转化到课堂上，教师参与者将开发新的课程材料，重点关注学生日常使用的许多应用程序的核心数学技术（例如，使用智能手机相机时在个人面部周围绘制框的ML算法）以及此类技术的伦理含义。这些材料将涉及印第安纳州教育部最新发布的计算机科学标准，并在地方、州和国家层面共享。通过这个RET网站的体验，教师将获得工具来介绍和准备下一代学生理解和开发ML/AI软件和系统。Computing RET网站将从大南本德地区的小学和中学招募教师队伍，以及与地区学区合作的几所当地大学，以形成在职/职前学生教师伙伴关系。一个具体的目标是将职前教师与在职教师配对，他们将与在职教师一起完成学生教学任务。选定的参与者将在夏季与ND的教师和学生一起进行为期七周的适当定制的ML/ ai中心研究项目。教师还将得到专业发展活动的支持，这些活动包括但不限于(1)将研究成果转化为课堂的策略，(2)向参与者介绍ML/AI硬件和软件方面的最新技术的演讲，(3)ML/AI系统带来的道德挑战等。开发的材料将通过多种渠道共享，包括计算机科学教师协会（CSTA）地区分会，以及CSTA或TeachEngineering.org等在线存储库。RET人员还将与当地学区的教育基金会合作，目的是在整个学区传播开发的材料。夏季活动将辅以广泛的项目后后续活动，这些活动共享一个总体主题，即帮助RET参与者向学生提供有关模块目标的科学背景和动机的背景。圣母大学的工作人员和在职教师还将与职前教师合作，讨论将STEM融入“日常”课程的策略和最佳实践，以及如何让不同群体的学生参与所述内容等。项目后的合作将确保将实验室经验转化为课堂实践，并促进和加强工程学院与当地学区之间的长期合作伙伴关系。该奖项反映了美国国家科学基金会的法定使命，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 award renews a Research Experience for Teachers site at the University of Notre Dame. The site will form partnerships between Notre Dame computer science students and faculty, Notre Dame students in pre-service education programs, and teachers from elementary and middle schools in the greater South Bend area. During the summer, teacher participants will work on research projects focusing on the advances, capabilities, and challenges associated with machine learning (ML) / artificial intelligence (AI) algorithms that are currently driving efforts across the technology, manufacturing, and government sectors. To translate their research experience into the classroom, teacher participants will develop new curricular materials that focus on the mathematical techniques at the heart of many applications students already use on a daily basis (e.g., ML algorithms that draw boxes around individual faces when using an smartphone camera) and the ethical implications of such technologies. These materials will address newly released computer science standards from the Indiana Department of Education and be shared at the local, state, and national levels. Through this RET site experience, teachers will gain tools to introduce and prepare future generations of students to understand and develop ML/AI software and systems. The Computing RET site will recruit teacher cohorts from elementary and middle schools in the greater South Bend area, as well as several local colleges that partner with area school districts to form in-service/pre-service student teacher partnerships. A specific aim is to pair pre-service teachers with the in-service teacher with whom they will complete their student teaching assignment. Selected participants will work with ND faculty and students for seven weeks during the summer on an appropriately tailored, ML/AI-centric research project. Teachers will also be supported by professional development activities that include but are not limited to (1) strategies for translating research into the classroom, (2) presentations that introduce participants to the technical state-of-the-art with respect to ML/AI hardware and software, (3) the ethical challenges posed by ML/AI systems, etc. Developed materials will be shared through multiple channels including a Computer Science Teachers Association (CSTA) regional chapter, and online repositories such as the CSTA or TeachEngineering.org. RET personnel will also engage with educational foundations in local school districts that aim to broadcast developed materials district-wide. Summer activities will be complemented by extensive, post-program follow-up activities that share an overarching theme of helping RET participants provide their students with context about the scientific background and motivation for a module objective. Notre Dame personnel and in-service teachers will also work with pre-service teachers to discuss strategies and best-practices for integrating STEM into “day-to-day” lessons, how to engage diverse groups of students with said content, etc. Post-program engagements will ensure the translation of lab experiences into classroom practice, and to foster and strengthen long-term partnership between engineering faculty and the local school districts.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.