Science

This paper explores a comprehensive framework to develop students’ data literacy by guiding them in making sense of complex data visualizations. With the growing complexity and prevalence of data visualizations in media, it’s crucial to equip students with the skills to critically analyze and engage with these visual forms of data. This toolkit emphasizes the importance of fostering data habits of mind, rather than mere computational proficiency, and encourages students to consider what a visualization is conveying, how it was created, and why it was created.

In this document, we identify key considerations to guide thinking and actions for data investigations, where the goal of an investigation is to answer a statistical question within a context to communicate approaches and solutions to a problem based on evidence. This process is composed of six phases: Frame the Problem, Consider and Gather Data, Process Data, Explore & Visualize Data, Consider Models, and Communicate & Propose Action.

When assisting students in a data investigation, it can be useful to help them develop key ways of thinking and dispositions that are helpful in developing expertise in conducting data investigations like a statistician or data scientist. It can be useful for students to see a reminder of this process and key considerations. This poster version of the Data Investigation Process that can be used in your classroom for this purpose.
 

Today, the ability to make sense of data is essential. K-12 students need educational experiences that can assist them in developing data literacy for global citizenry, and career and college pathways related to statistics and data science (e.g., Engel, 2017; Gould, 2017). Statistics and practices with data are included in standards and goals across the K-12 curriculum. Science puts a heavy emphasis on reasoning from and with data to understand scientific phenomena.

Developing the ability to do data science and make sense of the data science work done by others involves building skills and knowledge that span across all phases of a data life cycle and draw on many subject areas. Science tends to put a heavy emphasis on reasoning from and with data to understand scientific phenomena. English and Social Studies incorporate the use of data and data visualizations as evidence to support arguments, interpret information, and evaluate claims in social structures of our world.

Science instruction in elementary school provides a base for student understanding of the natural world, yet policies prioritizing mathematics and reading have marginalized science. In response, some teachers have enhanced their science instruction by introducing students to participatory science (PS) projects. Using data from a larger study that examines the development of educative support materials for two existing PS projects, this embedded mixed methods study focuses on teachers’ and students’ experiences learning outdoors.

This study centers the idea that it is not just what science teacher educators (STEs) teach, but how they teach it, that matters. To prepare future teachers who can enact more equitable and transformative reform-oriented science instruction with multilingual learners, research must explore what STEs are doing, and how, to develop preservice teachers' expansive views of language and understandings around the nuanced ways students might use their diverse language repertoires for sensemaking.