Are you wondering what kinds of data skills and ideas your students could be developing in your classroom?
The value of students developing data literacy is becoming recognized across disciplines, from STEM subjects to social sciences and even in language arts, with emphasis on communicating ideas and developing arguments. Despite the enthusiasm, deciding how to build data literacy as part of classroom instruction challenges many educators.
Asking questions is widely perceived as the first step in science. It’s listed as the first NGSS Science and Engineering Practice, and it is, indeed, often how a scientific investigation formally begins. Yet something does happen before a question takes shape. Questioning also continues to happen after an initial question is posed.
Here are a few thoughts in hopes to breathe life into the practice of asking questions throughout the process of gathering, exploring, and making sense of data.
(Photo by MI PHAM on Unsplash)
Over the past few years, web-based tools like Tuva and CODAP have made it easy for students to explore data in intuitive ways that keep them focused on what the data have to say rather than on spreadsheet mechanics. Many teachers use these tools with curated, fully-prepared datasets. But these tools can also be used to explore data that the students have collected themselves. However, we have found that many teachers encounter difficulty in organizing student data to work well with Tuva or CODAP. In this post, we use a hypothetical student study of birds on feeders to illustrate the issue and how to address it. Continue reading