These open source AI modules were developed through the NSF funded Science+C project. These modules were developed for high school students but can be easily adapted for community college students and may offer a starting point for those of you searching for AI coursework. View this video for an overview of the modules.
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).
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.
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.
The Investigating Immigration to the U.S. module focuses on describing, comparing, and making sense of categorical variables. Students investigate questions such as: Are there more immigrants in the U.S. today than in previous years? Where have immigrants to the U.S. come from, now and in the past? Are immigrants as likely as the U.S. born to be participating in the labor force, after adjusting for education?
The Investigating Income Inequality in the U.S. module focuses on describing, comparing, and making sense of quantitative variables. Students deepen their understanding of this content by investigating questions such as: How have incomes for higher- and lower-income individuals in the U.S. changed over time? How much income inequality exists between males and females in the U.S.? Does education explain the wage gap between males and females?
This poster provides an overview of the Strengthening Data Literacy across the Curriculum (SDLC) project, which is developing and studying curriculum modules for non-AP high school statistics classes to promote interest and skills in statistical thinking and data science among diverse high school populations. This early-stage design and development project aims to engage students with data investigations that focus on issues of social justice, using large-scale socioeconomic data from the U.S. Census Bureau and student-friendly online data visualization tools.
EDC's Oceans of Data Institute (ODI) has compiled a list of data activities, lessons, and resources for the classroom, sorted by grade level:
By lboghossian on February 04, 2019