Data Literacy

"Thinking Big," featured in the Summer 2015 issue of NSTA's The Science Teacher, explores curricular strategies for transitioning students to working with large, complex data sets. 

"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.

This presentation was given during the Exploratorium's Conversations About Landscape series on "From Data to Decisions: How Visualizations of Our Environment Inform Our Actions", held Tuesday, May 19th.

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 PowerPoint was presented at the Cutting Edge Digital Data Workshop in May  2015, and discusses the Oceans of Data Institute's work to-date.

Learn more about the workshop at http://serc.carleton.edu/NAGTWorkshops/index.html.

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 presentation at AAAS' 2015 Annual Meeting focused on how  teachers and instructional materials can help students transition from working with small, student-collected datasets to large, complex, professionally collected datasets. Strategies include minimizing extraneous information and maximizing insight-rich information in the data visualizations; leveraging the skill set that students bring with them from working with self-collected data; and practicing the use of spatial, temporal and quantitative reasoning to connect claims with evidence.

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