Data Literacy

Supporting students' work with authentic data requires a carefully developed and rigorously-tested curriculum to help them understand what the data represent, and to guide them in how to use data analysis tools and visualizations to identify meaningful patterns and develop evidence-based hypotheses.

About 20 years ago, psychologist Lynn Liben presented an model of the relationships among a learner, an external (i.e. not mental) representation, and those aspects of the real world represented by the representation (the “referent”). Liben notes that the learner can learn either through direct interactions with the real world or through interactions with a representation.

This paper describes our current thinking on pervasive and persistent understandings about data

Presentation at American Geophysical Union Conference in San Francisco, December 2013

Keynote Address, IBM's Big Data and Analytics EdCon 2013, November 2, Las Vegas

The world is awash with data. This growth of data shows no signs of slowing, and indeed seems to be accelerating. Analyzing data, spotting patterns, and extracting useful information have become gateway skills to full participation in the workforce and civic engagement of the 21^st century. Yet pre-college classrooms are falling short of preparing students for this world. And they’re missing the opportunity to harness the power of big data to transform student learning.

(June 2013)

Young children have the ability to reconstruct events based on the traces left behind by those events. Would nurturing this ability help them make meaning from data they did not collect themselves?...

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This talk focused on helping students span the transition from working with small student -collected datasets to large professionally-collected datasets....