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.
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.