Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships. The 7th grade unit involved students analyzing data graphs about adolescent friendships and crafting comic narratives to convey perspectives on that data.
To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool the Common Online Data Analysis Platform (CODAP).
To support preschool children’s learning about data in an applied way that allows children to leverage their existing mathematical knowledge (i.e. counting, sorting, classifying, comparing) and apply it to answering authentic, developmentally appropriate research questions with data. To accomplish this ultimate goal, a design-based research approach was used to develop and test a classroom-based preschool intervention that includes hands-on, play-based investigations with a digital app that supports and scaffolds the investigation process for teachers and children.
This collection of resources was generated by Data Pathways Community of Practice members—faculty and administrators from 2-and 4-year institutions building data programs. Learn more about ODI's work to support data programs at 2- and 4-year institutions in this 10-min video.
This article focuses on discussion and preliminary findings from classroom testing of the prototype learning module: Investigating Income Inequality in the U.S. In this module, students examine patterns of income inequality using person-level microdata from the American Community Survey (ACS) and the U.S. decennial census.
Zoom In! is a free, Web-based platform that helps high school students build their data literacy through “deep dives” into real-world biology and Earth science problems using authentic data sets. Each Zoom In blended learning module is a multi-day, standards-aligned science inquiry. Students use Zoom In digital supports as they read and analyze data to answer a scientific question, debate their interpretations, take notes and write a culminating argument supported by evidence.
Data literacy, or students’ abilities to understand, interpret, and think critically about data, is an increasing need in K–16 science education. Ocean Tracks College Edition (OT-CE) sought to address this need by creating a set of learning modules that engage students in using large-scale, professionally collected animal migration and physical oceanographic data to answer scientifically relevant questions and think critically about how researchers collect and interpret data.
By rKochevar on February 24, 2021
If the past month has done nothing else, it has shown us what a powerful force data can be in our daily lives. As the number of American lives lost from COVID passes half a million, state and county governments monitor the falling case rate data, which will determine when they can begin to re-open schools and businesses.
In Texas and across the Midwest, officials are having to come to terms with the fact that historical averages in weather patterns are not useful predictors of the conditions that occur during extreme weather events brought about by climate change.
To speed and ease the transition from education to employment in data fields, many community colleges are establishing data internships. Internships provide students with immediate opportunities to apply their data skills and knowledge to the tasks and problems challenging data workers in today’s workplaces. Internships benefit both students and employers. They provide students with opportunities to work on data teams, to learn to solve real-world data problems found in local industries, and to develop new data skills working in industry sectors that interest them.
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