Integrating the statistical investigation process, data visualization, and simulation into high school statistics

将统计调查过程、数据可视化和模拟整合到高中统计中

• 批准号: 2201121
• 负责人: Soma Roy
• 金额: $ 45万
• 依托单位: California Polytechnic State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201121&HistoricalAwards=false
• 关键词: Integrating; statistical; investigation; process; data

This project is developing curricular materials that utilize best teaching practices in improving student understanding of statistics and data science for use in high school Algebra I, Algebra II, and Geometry courses. Although teachers are encouraged to integrate statistics and data science in these kinds of high school courses, teachers do not have sufficient access to resources to accomplish this effectively. As a result, many high school students do not understand statistics for its relevance and importance in a data-driven world. This has created a significant need for curricular materials that use best teaching practices with documented impact on student learning. This project’s approach is to focus on meaning and interpretation rather than formulaic algorithms, which from students’ perspectives hide what matters. This project will develop curricular materials for high school students, with collaborative high school teachers for “how-to” manuals.The distinctive feature of these curricular materials is the use of simulation-based inference (SBI) methods, data visualization (DV), and the entire statistical investigation process (6STEPS) to improve students’ understanding of the relevance and power of statistics because these approaches are central to statistical thinking and practice. These approaches (as advocated by the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education) focus more on meaning and less on mechanics. This project enacts a pilot set of instructor training and student materials tailored to the HS level which use best pedagogical practices (e.g., designed with multiple learning styles in mind, effective use of technology, data visualizations) which seek to improve students’ statistics education at the high school level, while also providing detailed assessment data exploring the effectiveness of SBI, DV, and 6STEPS approaches to statistical education in high school. The project provides critical information about the ability of these teaching approaches to have a positive impact in the HS classroom. The project directly enrolls 60 HS teachers and about 1,800 students. The data collected is providing information about conceptual learning outcomes, student attitudes, teacher confidence, and learning trajectories using the SBI, DV and 6STEPS approaches. The research question that guides the project is: To what extent are newly developed, high school student-facing curricular materials, which leverage SBI, DV, and the 6STEPS, along with an active-learning pedagogy, able to integrate into existing high school courses (e.g., Algebra I, II, Geometry) and lead to improved student learning outcomes? The project supports a website that shares 24 freely available student-facing and instructor-facing modules, as well as professional development resources. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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.

该项目正在开发课程材料，利用最佳教学实践提高学生对统计和数据科学的理解，用于高中代数I，代数II和几何课程。 虽然鼓励教师将统计和数据科学融入这些高中课程，但教师没有足够的资源来有效地完成这一任务。因此，许多高中生不了解统计数据在数据驱动的世界中的相关性和重要性。 这就产生了对使用最佳教学实践并对学生学习产生有记录影响的课程材料的重大需求。这个项目的方法是关注意义和解释，而不是公式化的算法，从学生的角度来看，隐藏了什么是重要的。该项目将为高中学生开发课程材料，与高中教师合作编写“操作”手册。这些课程材料的显著特点是使用基于模拟的推理（SBI）方法，数据可视化（DV），整个统计调查过程（6STEPS）提高学生对统计相关性和力量的理解，因为这些方法是统计思维和实践的核心。这些方法（如美国统计协会的统计教育评估和指导方针所倡导的）更多地关注意义，而不是机制。该项目颁布了一套针对统一制度一级的教员培训和学生材料的试点，这些材料采用了最佳教学做法（例如，设计考虑到多种学习风格，有效利用技术，数据可视化），旨在提高学生在高中阶段的统计教育，同时还提供详细的评估数据，探索SBI，DV和6STEPS方法在高中统计教育中的有效性。该项目提供了关键信息，这些教学方法的能力，在HS课堂上产生积极的影响。该项目直接招收60名HS教师和约1，800名学生。所收集的数据提供了有关概念学习成果，学生的态度，教师的信心，并使用SBI，DV和6STEPS方法的学习轨迹的信息。指导该项目的研究问题是：在多大程度上是新开发的，面向高中学生的课程材料，利用SBI，DV和6STEPS，沿着主动学习教学法，能够融入现有的高中课程（例如，代数一，二，几何），并导致提高学生的学习成果？该项目支持一个网站，分享24个免费提供的面向学生和面向教师的模块，以及专业发展资源。探索研究preK-12计划（DRK-12）旨在通过研究和开发创新资源，模型和工具，显着提高preK-12学生和教师的科学，技术，工程和数学（STEM）的学习和教学。DRK-12项目中的项目建立在STEM教育的基础研究以及为拟议项目提供理论和经验依据的先前研究和开发工作的基础上。该奖项反映了NSF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。