The term ‘data literacy’ casts a wide net. A web search brings up data science, big data, statistics, AI and algorithms, student assessment data, analytics, and many other applications of data in business, education, workforce training, and global institutions.
We are centered on the fundamental challenge of building basic skills and habits of thought and inquiry in young students as they learn to gather, interrogate, and visualize data, and derive useful information from it.
We want students to be able to collect observations, organize those data in tables and spreadsheets, visualize data in graphs to hunt for patterns, interpret patterns in the context of a problem or question, and reason about data as evidence to generate and evaluate new information and new questions. We want them to be able to illustrate and tell the stories they find in data, and apply what they learn to make evidence-based decisions.
Doing all of that involves a unique set of “literacy” skills that draws from mathematics, visual skills, science practice, knowledge of subject matter, language, technology, reasoning, and storytelling. Students often study these skills as separate disciplines, but the practice of putting them together to read, write, and speak coherently about data as evidence and derive meaning in doing so, is often short-changed or overlooked in school curricula.
For us, data literacy is a synthesis of interdisciplinary skills and knowledge applied to develop understanding, deepen inquiry, and make decisions that matter and are supported by evidence.