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

这一奖项更新了圣母大学的教师研究经验网站。该网站将在圣母大学计算机科学专业的学生和教师、参加职前教育项目的圣母大学学生以及大南本德地区的中小学教师之间建立合作伙伴关系。在暑假期间，教师参与者将致力于研究项目，重点是与机器学习(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，使用智能手机摄像头时在个人面部周围画框的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、州和国家各级共享。通过这次RET现场体验，教师将获得介绍和准备未来一代学生理解和开发ML/AI软件和系统的工具。计算RET网站将从大南本德地区的小学和中学以及与该地区学区合作形成在职/职前学生教师合作伙伴关系的几所当地大学招聘教师队伍。一个具体的目标是将职前教师与将与他们一起完成学生教学任务的在职教师配对。选定的参与者将在暑假期间与ND教职员工和学生一起工作七周，进行适当定制的、以ML/AI为中心的研究项目。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研究的战略；(2)向学员介绍关于ML/AI硬件和软件的最新技术；(3)ML/AI系统带来的伦理挑战等。编写的材料将通过多种渠道分享，包括计算机科学教师协会(Csta)地区分会以及Csta或TeachEngineering ering.org等在线资料库。RET人员还将与当地学区的教育基金会接触，这些基金会的目标是在全区范围内广播开发的材料。暑期活动将与广泛的计划后后续活动相辅相成，这些活动都有一个共同的主题，即帮助RET参与者向他们的学生提供关于模块目标的科学背景和动机的背景知识。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整合到日常课程中的策略和最佳实践，如何让不同群体的学生参与到上述内容中来，等等。项目后的参与将确保将实验室经验转化为课堂实践，并培养和加强工程学教师与当地学区之间的长期合作伙伴关系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.