Integrating Computational Science with Environmental Sciences Associated with Habitat Restoration and Education in New York Harbor

由于计算已经成为科学、技术、工程和数学（STEM）实践中不可或缺的一部分，STEM+计算计划旨在通过将计算思维和计算活动应用于高中（preK-12）早期儿童教育的STEM教学和学习，来解决计算STEM领域的新挑战。该项目由STEM+C项目支持，通过将计算思维、数据素养和统计概念与面向中学生的基于实地的STEM课程模型相结合来推进其使命，该课程侧重于与栖息地监测和恢复相关的科学领域。这一整合将通过以下方式实现：(1)扩大和进一步发展纽约港水域内和附近的恢复中心，以支持实地科学体验；(2)为教师提供专业学习机会，将计算思维与其他STEM学科相结合；(3)开发课程单元，促进计算机科学和数据科学概念和技能的学习，并提高对计算机、数据科学和生境恢复科学职业教育途径的认识；(4)计算机科学与计算暑期STEM学院。这个项目将扩大一个大型设计和开发项目，该项目正在测试城市环境中以社区为基础的科学教育模式。现有的课程模式侧重于地质、环境和生物科学中与监测环境条件和栖息地恢复有关的关键概念。该项目将通过将数据素养、计算思维和相关统计概念和技能纳入现有课程来加强现有模式，使学生能够获得与收集和分析与环境监测和栖息地恢复有关的大量实地数据有关的新技能和能力。要分析的数据范围从基本的水化学数据到与细菌监测和环境DNA采样和分析相关的数据。与本项目相关的研究以三个假设为指导：(1)教师在数据素养、计算机科学和STEM实践方面的专业学习将积极影响学生在课堂上的学习成果；(2)以教师在数据素养、计算机科学和科学研究实践方面的专业学习为基础的教学，加上教师和学生与科学家的接触，将积极影响学生对计算思维的认识以及对STEM领域和职业的看法；(3)项目的参与模式，包括基于问题的学习，将提高学生的意识和意愿，追求STEM职业的教育途径。将对实验组和对照组的教师和学生采用各种措施来检验这些假设。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。

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). This project is supported by the STEM+C program and advances its mission by integrating computational thinking, data literacy, and statistical concepts with a field-based STEM curriculum model for middle school students that focuses on science fields associated with habitat monitoring and restoration. This integration will be accomplished through: (1) Expansion and further development of Restoration Hubs in and near the water of New York Harbor that support field science experiences; (2) Professional learning opportunities for teachers that feature integration of computational thinking with other STEM disciplines; (3) Development of curriculum units that promote learning of computer science and data science concepts and skills, and raise awareness of education pathways to careers in computing, data sciences, and habitat restoration sciences; and (4) A summer STEM Institute for computer science and computing.This project will augment a large-scale design and development project that is testing a model of community-based science education in an urban environment. The existing curriculum model focuses on key concepts in the geological, environmental, and biological sciences that are associated with monitoring environmental conditions and habitat restoration. This project will enhance the existing model by integrating data literacy, computational thinking and relevant statistical concepts and skills into the existing curriculum, enabling students to gain new skills and competencies associated with gathering and analyzing large amounts of field data associated with environmental monitoring and habitat restoration. Data to be analyzed range from basic water chemistry data to data associated with bacterial monitoring and environmental DNA sampling and analyses. The research associated with this project is guided by three hypotheses: (1) Teacher professional learning in data literacy, computer science, and STEM practices will positively influence student outcomes within their classrooms; (2) Teaching informed by teacher professional learning in data literacy, computer science, and science research practices, coupled with teacher and student engagement with scientists will positively influence student knowledge of computational thinking and perceptions of STEM fields and careers; and (3) The project's model of engagement, including problem-based learning, will enhance student awareness of and intent to pursue education pathways to STEM careers. A variety of measures will be used with teachers and students in treatment and comparison groups to test these hypotheses.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.