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Saturday, May 19
Data Science
Data Science in Practice
Sat, May 19, 10:30 AM - 12:00 PM
Grand Ballroom G

Data Moves in Data Science Education (304473)


*Tim Erickson, Epistemological Engineering 
Bill Finzer, The Concord Consortium 
Frieda Reichsman, The Concord Consortium 
Michelle Wilkerson, University of California at Berkeley 

Keywords: education, data moves, tools

In an NSF-funded project (Data Science Games), we are exploring how to teach the basic ideas and practices underlying data science at the high-school level. In that work, we have identified core ideas we call “data moves” --- fundamental data actions such as creating groups, defining aggregate measures, and looking at subsets by filtering data. Current software makes these moves easy, almost transparent, which leads to a problem: when students learn the mechanics of these moves, they often do not understand what’s really happening. This is a barrier to truly grasping data science, and can lead to unnecessary student attrition and shallow understanding. We believe that with the right methods and tools, these data moves are accessible, without coding, to high-school students and older learners. If students truly understand grouping or aggregation, they will be better consumers of data science and more informed members of the broader public. Furthermore, students who have an understanding of the nature of data and data moves will be better prepared to acquire the coding skills that they will need to become data science practitioners. This paper shares our current thinking on this topic, including specific examples and tools we have developed to approach this challenge.