This project will develop an approach to support fourth grade students' data literacy with complex, large-scale, professionally collected data sets. The work will focus on analytical thinking as a subset of data literacy, specifically evaluating and interpreting data. The project will teach students about working with geoscience data, which connect to observable, familiar aspects of the natural world and align with Earth science curriculum standards.
These skills are essential for working with scientific data sets, but educators know very little about how to prepare students for the issues involved in making appropriate inferences from data. The need is compounded by the fact that studies that exist have worked with data sets that students themselves collected, whereas the many electronic data sets, proliferating in the public domain, pose different challenges. This project will develop an approach to support fourth grade students' data literacy with complex, large-scale, professionally collected data sets. The work will focus on analytical thinking as a subset of data literacy, specifically evaluating and interpreting data. The project will teach students about working with geoscience data, which connect to observable, familiar aspects of the natural world and align with Earth science curriculum standards. An interdisciplinary team of educators, researchers, and scientists from the Oceans of Data Institute at Educational Development Center and the American Geological Institute will (1) conduct baseline research to understand students' natural affinities for understanding inference from complex data and phenomena; (2) develop and test scaffolding activities that leverage students' intellectual assets and minimize barriers to analytical thinking with professionally collected data; and (3) examine the degree to which the resulting activities support students to do productive work with professionally collected data. In developing an instructional approach, the project informs generally how professionally collected, scientific data can be used to support elementary students to develop data literacy skills.
Hypothesizing that science, technology, engineering, and mathematics (STEM) education generally can benefit from the instructional use of complex, large, interactive, and professionally-collected (CLIP) data sets (e.g., related to precipitation, stream flow, and groundwater levels), this study will explore approaches to integrating those data into fourth grade classroom instruction. The research is based on a premise that students who engage with CLIP data early in their classroom STEM experiences will develop skills and attitudes that promote meaningful analyses of those data earlier than if that exposure is delayed until secondary courses. The project will use a three-phase iterative design that will unfold in three urban and suburban school districts in Virginia and Maryland. Phase one will focus on creating a baseline of the reasoning students employ when making inferences from data. It will involve 45 students from grades 3-5 in targeted interviews, which will be recorded, transcribed and analyzed. Phases two and three will focus on design and development in grade 4. Phase two will develop and test activities through an iterative design plan that employs a semi-clinical method with small groups of students. Phase three will implement the activities that result from that process in six classrooms across three districts with approximately 150 students. A scoring rubric that captures student learning will be constructed in phase two and used to measure impacts of the field testing in phase three. Observations and interviews will also be conducted at field sites to understand what students learn about analytical thinking from the activities.