We define data literacy as the ability to visually explore and analyze data in various forms, ask questions of data, and use it as evidence for argumentation and decision making.
The jobs that will be available to today’s middle and high school students when they complete their education will involve working with data. Workers in all sectors will need to be able to collect, organize, visualize, analyze, and/or make decisions based on data. Even now, mechanics, teachers, technicians, drivers, journalists, retailers, designers, and health care providers work with data routinely. Just being a citizen involves understanding and interpreting data related to buying, voting, staying healthy, and finding financial security. For many students, middle and secondary school will be their only opportunity to develop fundamental data skills.
At Tuva, we also believe that a new type of literacy demands a new set of tools.
A critical aspect of data literacy is to help our students understand that the world is inherently variable. Understanding the world around us hinges on the ability to perceive how it varies and to recognize that variability engenders some uncertainty. The tools that are available today are limited in their ability to help students investigate and understand variability.
In elementary school, students learn to summarize a group of numbers with a single value such as a total or an average, which they often plot as a bar graph. In reducing measurements to a single value, a bar graph obscures how much a group varies. A fundamental challenge of data literacy begins in early middle school when students learn to informally anticipate, describe, and explain variability when interpreting data. To acknowledge variability, students need to learn how to look at data as distributions – dot plots, box plots, and histograms.
Tuva’s technology is designed so students can explore data and look for patterns without spending valuable class time fussing with columns and rows and constructing graphs in a spreadsheet.
Once students understand the different kinds of graphs they can create and they have access to tools to be able to do it easily, they are ready to learn how to make active decisions about how to explore and analyze the data depending on what they are trying to find out. The way a question is worded gives clues about how to graph data – what kind of graph to use, what attributes to put on the axes, and which subsets of a dataset to include (or not). Tuva provides a framework for reasoning through how best to graph data to keep students at the center of the process of graphing and analysis. If students are told how to graph data, they can’t learn how the purpose of an inquiry (the question) relates to deciding how to proceed with graphing and analysis.
Once students are familiar with different kinds of graphs and have access to tools to create them easily, they are ready to learn how to make active decisions about what kind of graph to use in a given situation.
Data literacy requires students to take reasoned steps as they make sense of patterns in data. We expect students, with scaffolding and guidance along the way, to ultimately be in the driver’s seat when it comes to using data to investigate questions, compile evidence, and respond to problems with possible solutions based on evidence. We expect data-literate students to make reasoned decisions about how to interpret and present evidence, and to be able to critique, revise, and reconsider in light of new data – to think on their feet as the world changes around them.