Data Skills

The Oceans of Data Institute (ODI) at the Education Development Center (EDC), Inc.; Stanford University; and the Scripps Institution of Oceanography have been collaborating, with the support of three National Science Foundation grants over the past 5 years, to bring large scientific data sets into secondary and postsecondary classrooms.

"Harvesting a Sea of Data", featured in the Summer2015 issue of NSTA's The Science Teacher, addresses the fundamental challenge in getting big data into K-12 education: how to build a good interface. The article discusses the work of Ocean Tracks, an innovative program that gives students access to authentic data to investigate marine migrations.

Today’s students will graduate into a world where oceans of data are available to influence and drive decision making. When the Oceans of Data Institute (http://oceansofdata.org) surveyed 300+ students from community college and university settings, 85% of respondents agreed or strongly agreed that the ability to make sense of data is important to get a good job and will help in their future careers. An overwhelming 90% of respondents agreed or strongly agreed that learning to make sense of data will help them be more effective and informed citizens.

This presentation was given at the Gulf of Maine Research Institute (GMRI) in February of 2015.

Explore the Powerpoint to learn more about the Ocean Tracks project, including background information, goals, and findings to-date.

This poster was presented at the American Geophysical Union's (AGU) Fall meeting in 2014 to describe the DACUM process and present the finalized occupational profile.

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.

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....