Inclusive Data Science Education for Rural Elementary Students: A Research Practice Partnership for Agile Learning

需要可扩展和敏捷的方法来激励年轻学习者培养STEM和计算机科学素养，并提高对STEM和计算机科学职业的兴趣。然而，提高STEM和计算机科学技能在小学尤其具有挑战性，因为教师经常教授他们准备之外的科目，技术支持有限，计算机科学课程资源有限。在农村地区，地理上的隔离和贫困进一步加剧了现有的障碍，残疾学生在STEM学科上比同龄人更加困难。因此，这些学生发展计算机科学（CS）和计算思维（CT）技能的机会从根本上是不公平的。该研究实践者伙伴关系项目旨在为农村小学生提供数据科学教育，包括患有高发残疾（如学习障碍、情绪/行为障碍）的学生，以增加对计算机科学教育的参与，拓宽磨练计算机科学技能的途径。项目团队将通过克莱姆森大学的研究人员和农村学区的4 -5年级教师之间的合作来实现这一目标。研究人员和教师将共同开发、实施和测试一个模型，用于创建和维持一个可定制的学习模块，该模块侧重于在STEM背景下发展CT技能。该团队将采用基于设计的实施研究方法进行项目，他们将迭代地共同设计课程资源并进行研究，以通知课程的修订。他们还将使用“弹出式”方法来满足对可扩展和敏捷数据科学课程模块的需求。在教育领域，pop -up通常被理解为可定制的课程或单元，这些课程或单元长度不同，并根据学生的需求在不同时间实施；它们通常最适合教授一门新的技能或技术。在这个项目中，Pop-up模块将被设计成(1)提供当地情境化的问题和议题；(2)符合南卡罗来纳州的《计算机科学和数字素养标准》；(3)映射到基于研究的数学和科学课堂CT实践分类（Weintrop et al., 2016）；(4)吸引农村的年轻学习者，包括残疾学生。该团队将使用互联学习理论和通用学习设计框架来指导课程开发工作。通过并行混合方法，他们将调查共同设计课程过程的关键特征、教师在数据科学课程迭代实施周期中的成功和挑战、项目和课程对教师信心、自我效能感和兴趣的影响、课程对小学生数据和计算问题解决实践的影响。对残疾学生和非残疾学生的影响是否不同，对学生对数据科学的信心、自我效能感和兴趣的影响。该项目团队将与美国各地的研究人员和从业者分享他们开发的模型，以改善在机会和资源方面一直处于劣势的学生的STEM学习。教师们共同创建的数据科学模块将在网上提供给全国各地的教师下载和定制。该项目由CS for All: Research and RPPs项目资助。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。

Scalable and agile approaches are needed to inspire young learners to develop STEM and computer science literacies and increase interest in STEM and computer science careers. However, advancing STEM and computer science skills is particularly challenging in elementary schools where teachers often teach subjects outside of their preparation, have limited technology support, and limited computer science curricular resources. In rural areas, geographical isolation and poverty further exacerbate existing barriers, and students with disabilities struggle significantly more than their peers in STEM disciplines. As a result, opportunities to develop computer science (CS) and computational thinking (CT) skills for these students are fundamentally inequitable. This Research Practitioner Partnership project is aimed at making data science education accessible to rural, elementary students, including students with high-incidence disabilities (e.g., learning disabilities, emotional/behavioral disorders), to increase participation in CS education and broaden ways to hone CT skills. The project team will accomplish this through collaborative work between Clemson University researchers and 4th-5th grade teachers from a rural school district. The researchers and teachers will work together to develop, implement and test a model for creating and sustaining a customizable learning module that focuses on developing CT skills within a STEM context. The team will take a Design-Based Implementation Research approach to the project, where they will iteratively co-design curricular resources and conduct research to inform revisions to the curriculum. They will also use a “Pop-up” approach to address the need for scalable and agile data science curriculum modules. In education, Pop-ups are often understood as customizable courses or units that vary in length and are implemented at various times based on student needs; they are often best suited to teaching a new skill or technology. In this project, the Pop-up modules will be designed to (1) provide local contextualized problems and issues; (2) align to South Carolina’s Computer Science and Digital Literacy Standards; (3) map to a research-based taxonomy of CT practices for mathematics and science classrooms (Weintrop et al., 2016); and (4) appeal to young rural learners, including those with disabilities. The team will use Connected Learning Theory and the Universal Design for Learning framework to guide the curriculum development work. Through a concurrent parallel mixed-methods approach, they will investigate the key features of the co-design curriculum process, teachers’ successes and challenges during iterative implementation cycles of the data science curriculum, the impact of the project and curriculum on teachers’ confidence, self-efficacy and interest, the impact of the curriculum on elementary students’ data and computational problem-solving practices, whether the impact is different for student with and without disabilities, and the impact on students’ confidence, self-efficacy and interest in data science. The project team will share a model that they develop with researchers and practitioners across the United States to improve STEM learning for students who historically have been at a disadvantage in terms of access and resources. The data science modules that the teachers co-create will be available online for teachers across the country to download and customize. This project is funded by the CS for All: Research and RPPs program.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.