Research

The Oceans of Data Institute research agenda aims to understand how humans make meaning from data. How is it possible that we can look at numbers, or dots, or squiggles, or blotches of color and make inferences about phenomena in the real world of which the data are a representation? How do experts do this? How can students learn to do so? And how can teachers help them?

The figure below sketches the broad outline of a plausible sequence of stages that we hypothesize may lead to an adult who can use data powerfully in professional or personal life. We envision this as a long process, spanning many years of maturation and education.

Data Learning Progression

 

At first, children observe the world around them with their human senses, developing an ability to make inferences about structures and phenomena that they experience directly (A). Next, as students, they work with small datasets that they have collected themselves (B), such as a map that they made of a stream near their school. Later, they work with larger datasets that they did not collect (C). At first, they work on fairly well-defined problems, problems to which their teacher knows the answer. And then, finally, they learn to work with large datasets around ill-structured problems (D), the sorts of problems characteristic of adult life. We envision this trajectory as having intervals of gradually increasing proficiency (labeled “business as usual” in the figure) when the learner is within one of these domains, interrupted by transitions when the learner must make big steps in learning.

The Oceans of Data Institute's R&D agenda spans all phases of this proposed learning progression. We have proposed work with pre-K through third graders that will probe how these young students lay the cognitive groundwork for interpreting datasets that they did not collect themselves. Data Puzzles and the data-using activities of EDC Earth Science are designed to scaffold students and teachers across Transition II and into the earliest explorations of professionally collected datasets by using carefully selected snippets of high insight-to-effort ratio data. The Ocean Tracks and FIRE: Making Meaning from Geoscience Data projects both work within domain (C), watching as students view and interpret data visualizations made from global data archives. Ocean Tracks research is classroom-based, whereas FIRE is laboratory-based. The EarthCube project seeks to move geoscience students across Transition III by providing them with both computational and intellectual tools to tackle problems that need multiple complex datasets. 

Implicit in the model above is the idea that there are persistent meta-understandings about data and its relationship to the represented system, understandings that build across the trajectory from unstructured observations with the human senses all the way through to large datasets and ill-structured problems. Read our current thinking on

Student Eye Tracker

A student views and interprets data visualizations of ocean salinity in the Mediterranean Sea and adjacent North Atlantic. As she answers questions about what she sees, her utterances and gestures are videotaped, and her gaze is recorded by an eye-tracking device in the frame of the computer monitor.