Collaborative Research: Exploring the Mathematics of Biological Ecosystems with Data Science

使用软件工具来探索、分析、可视化和解释数据越来越普遍。然而，数据科学技能和实践的发展在许多K-12教育中仍然不常见。这个项目将创建和研究“数据旅行”，这是一种结构化的活动，旨在培养高中生在现有的、基于现象的科学课程中进行数据实践和建模的能力。整合了通用在线数据分析平台(CODAP)，这是一个免费的基于网络的数据分析和建模工具，专为6-14年级的学生设计，数据旅行将允许学生与从初级研究中提取的现有数据集进行互动。学生将学习如何聚合和表示数据来解释生物现象，如耐抗生素细菌的传播和塞伦盖蒂平原的种群变化。该项目将使用基于设计的研究来调查使用真实数据集、创建计算表示并使用它们来推理生态系统现象如何支持学生数据科学素养的发展。将使用迁移任务、课堂观察、学生访谈和简短的离职调查来确定干预措施的哪些方面会导致预期的学习结果。研究人员还将为教师开发专业学习机会，帮助他们为来自非主导社区的学生创建相关和连贯的材料。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。

Using software tools to explore, analyze, visualize, and explain data is increasingly common. However, the development of data science skills and practices are still not common in much of K-12 education. This project will create and study "data excursions," structured activities that develop high school students' competencies with data practices and modeling within existing, phenomenon-based science curriculum. Integrating the Common Online Data Analysis Platform (CODAP), a free web-based data analysis and modeling tool designed for students in grades 6 – 14, the data excursions will allow students to interact with existing datasets drawn from primary research. Students will learn to aggregate and represent data to explain biological phenomena such as the spread of antibiotic-resistant bacteria and population changes in the Serengeti plains.The project will use design-based research to investigate how working with real datasets, creating computational representations and using them to reason about ecosystem phenomena can support students' development of data science literacy. Transfer tasks, classroom observations, student interviews, and short exit surveys will be used to identify what aspects of the interventions lead to desired learning outcomes. The researchers will also develop professional learning opportunities for teachers to help them create relevant and coherent materials for students from nondominant communities. 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.