Teaching Students to Reason about Variation and Covariation in Data: What Do We Know and What Do We Need to Find Out?

由于数据是现代生活的一部分，因此理解数据是一项使所有社会成员受益的技能。数据推理被描述为最重要的认知活动之一，理解数据对于公众知情的公民参与至关重要。但公众理解数据的能力并不是它应该的那样。教育工作者在支持学生成为数据精明消费者的能力（最终是公众的能力）方面发挥着重要作用。但教育研究人员缺乏对当前支持学生分析，解释和推理数据的最佳实践的一致看法。现有的研究集中在支持学生学习分析，解释和推理数据往往驻留在筒仓由年级带和数学或科学领域。该项目的目的是收集，分析和综合从1988年至今发表的数学和科学教育研究，这些研究调查了支持6-14年级学生学习分析，解释和推理数据的不同方法。研究人员将仔细研究每种教育干预的性质以及研究人员在每种情况下的发现，寻找跨研究的模式。 本研究之发现，可供数学与科学教育发展者在制作教师与学生的教学计划时参考。 研究人员将收集、分析和综合1988年至今的数学和科学教育研究，研究与数据变化和协变相关的教学。该小组将首先进行描述性综合，包括广泛的研究（定性，单组前/后，实验/准实验），并检查在该领域的干预措施的性质。接下来，研究人员将使用稳健方差估计（RVE）对实验和准实验进行统计元回归，以研究主要研究的效应大小估计如何取决于干预特征，研究设计，感兴趣的结果和研究参与者的人口统计学特征。 该项目将帮助数学和科学教育领域的研究人员相互借鉴，最终开发和完善高效的方法，以支持学生在复杂世界中理解数据的终身技能。发现研究K-12计划（DRK-12）旨在显著提高科学，技术，工程和数学（STEM）的preK-12学生和教师，通过研究和开发创新的资源，模型和工具。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。

Because data are so much a part of modern life, making sense of data is a skill that benefits all members of society. Reasoning about data has been described as one of the most important cognitive activities and making sense of data is essential for a public's informed civic participation. But the public's ability to make sense of data is not what it should be. There is an important role for educators to play in supporting students' ability (and ultimately the public's ability) to be savvy consumers of data. But education researchers lack a coherent vision of the current best practices for supporting students in analyzing, interpreting, and reasoning about data. Existing research focused on supporting students in learning to analyze, interpret, and reason about data tends to reside in silos by grade band and by math or science domain. The purpose of this project is to gather, analyze, and synthesize mathematics and science education research studies published from 1988 to the present that have investigated different approaches to supporting students in grades 6-14 in learning to analyze, interpret, and reason about data. The researchers will carefully examine the nature of each education intervention and what the researchers found in each case, looking for patterns across studies. The findings of this study can inform mathematics and science education developers in the production of instructional programs for teachers and students. The researchers will gather, analyze, and synthesize studies in mathematics and science education from 1988 to the present that examine instruction related to variation and covariation in data. The team will first conduct a descriptive synthesis including a wide array of studies (qualitative, single group pre/post, and experimental/quasi-experimental) and examine the nature of interventions in the field. Next, researchers will conduct a statistical meta-regression of experiments and quasi-experiments using Robust Variance Estimation (RVE) to examine how effect size estimates from primary studies depend on intervention characteristics, study design, outcomes of interest, and demographic characteristics of participants in the studies. The project will help researchers across math and science education build on each other's work and ultimately develop and refine highly effective approaches for supporting students in the life-long skill of making sense of data in a complex world.The Discovery Research K-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.