Assessing Quantitative Reasoning in a Ninth Grade Science Class Using Interdisciplinary Data Story Assignments

Abstract

In a data-driven world, it is necessary that students graduate from high school quantitatively literate, with the ability to interpret quantities within a context to make informed decisions for their lives. A critical component of science learning is developing the ability to make sense of data, critically evaluate it, and effectively communicate scientific ideas. The purpose of this study is two-fold: 1) to investigate how 9th grade students in an Earth Science class use quantitative reasoning (QR) skills when constructing evidence-based scientific explanations during Data Story assignments and 2) to provide teachers with supports to incorporate Data Stories into their curriculum. A Data Story is an interdisciplinary, scaffolded written argumentation assignment that requires students to analyze authentic, real-world scientific data and draw their own conclusions. In doing so, students integrate several discrete skills to synthesize an argument that is supported by evidence. Quantitative and qualitative results were used to investigate affordances and challenges students face when constructing a Data Story, what QR skills they use in the process, and what aspects of QR are challenging for them. Two evidence-based learning progressions provided the foundation for the development of two rubrics to score the student Data Stories quantitatively. Four student interviews analyzed using Grounded Theory provided qualitative insight into the role of QR in evidence-based explanations. Results suggest students enjoyed the Data Story assignments, which exposed them to a range of graph-types and data literacy skills. However, students seemed to struggle to develop appropriate evidence to support a claim in the Claim-Evidence- Reasoning (CER) framework and may need additional supports in this area. Further analysis with the QR Rubric and student interviews revealed some aspects of QR that may be hindering science learning and the development of evidence-based reasoning including: 1) not reasoning about variables in the context of a dataset 2) looking only for a correlation or difference and 3) not using quantitative language. These are aspects teachers should consider when implementing Data Story assignments in their own classrooms as a way to enhance students’ abilities in developing appropriate evidence to support a claim