Collaborative Research: An Equity-Focused Approach to Integrating Physical Activity and CS Education for K-8 Learners

该项目通过Project SMART为一所以拉美裔人口为主的小学带来计算和数据科学方面的指导，这是一款数字教育游戏，利用学生在学校的体育活动来激励学生学习。学生们记录他们在上学期间的体育活动，无论是在体育课上、课间休息时，还是在老师提供的其他体育活动机会中。一个小学班级合作玩耍；班上所有学生的身体活动被转换为沿着延长的虚拟旅程旅行的距离。学习模块由教师定义，与核心课程直接相关。该方法的关键是提供的计算性学习活动引入了使用学生自己的身体活动数据的数据收集、分析和预测问题。因此，该项目探索的研究问题正在推动该领域对如何让学生参与计算机科学的理解。它还在调查，如果学生的计算思维学习目标与他们相关的数据有关：他们的身体活动、他们的兴趣、他们的环境，更重要的是他们自己收集的数据，那么这些目标是否更多地参与到计算思维学习目标中，并在这些目标上取得成功。该项目扩展和加强了项目智能研究人员-实践者伙伴关系(RPP)，让研究人员和教师共同努力，确定计算机科学和计算思维教学的策略，吸引小学生群体，解决教学挑战。这些挑战包括遵守国家标准的压力，这使得整合更多内容变得困难，缺乏限制校外学生参与的家庭互联网基础设施，以及教师对计算机科学内容知识缺乏经验和信心。以研究计划为导向的推测阐明了一个前提，即将计算机科学融入学生在正常上学日的身体活动中，作为一门综合的核心学科课程，将有助于应对这些挑战。除了促进该领域对数据科学教学情景方法的理解外，该项目还创建了可共享的学习模块，提供数据科学学习活动，使用学生自己的身体活动来实现数据建模和预测；指导学生在硬件-软件联合设计中创建可穿戴的身体活动监视器，以改进数据收集；并使学生能够编写代码，扩展游戏中可用的功能。该项目由CS for All：Research and RPPS计划资助。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 project brings instruction in computing and data science to an elementary school with a predominantly Hispanic population through Project SMART, a digital educational game that uses students’ in-school physical activity to motivate student learning. Students record their physical activity during the school day, whether in physical education class, at recess, or at other teacher-provided opportunities for physical activity. An elementary school class plays collaboratively; the physical activity of all the students in the class is converted to a distance traveled along an extended virtual journey. The learning modules are defined by teachers and directly connected to the core curriculum. Key to the approach is that the computational learning activities provided introduce data collection, analysis, and prediction questions using the students’ own physical activity data. As such, the project explores research questions that are advancing the field's understanding about how to engage students in computer science. It also is investigating whether students are more engaged in, motivated by, and successful at computational thinking learning objectives if these objectives relate to data that is relevant to them: their physical activity, their interests, their environment, and, importantly, their own collected data.This project expands and strengthens the Project SMART researcher-practitioner partnership (RPP), engaging researchers and teachers in collaborative efforts to identify strategies for computer science and computational thinking instruction that engages an elementary student population and addresses instructional challenges. These challenges include pressures to adhere to state standards that make it difficult to integrate additional content, a lack of at-home internet infrastructure that limits out-of-school student engagement, and teachers' lack of experience with and confidence in computer science content knowledge. Conjectures that orient the research plan explicate a premise that contextualizing computer science within the students' own physical activity during the regular school day as an integrated core subject lesson will help to address these challenges. In addition to advancing the field's understanding of a contextualized approach to data science instruction, this project also creates shareable learning modules that present data science learning activities that use the students' own physical activity to enable data modeling and prediction; guide students in hardware-software co-design to create wearable physical activity monitors that can improve data collection; and enable students to write code that extends the functions available in the game. 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.