Digital APUSH

Using Data to Investigate the Past

This page originated at apush.omeka.net. ≈ Digital APUSH is the product of Advanced Placement United States History students at Sunapee High School in Sunapee, New Hampshire. Completed each May following the AP Exam, these digital history projects introduce students to alternative methods of historical research, concentrating on text analysis, data collection, and the creation of data visualizations.

With each annual project it is hoped that students make a small but original contribution to historical scholarship. However, the projects are primarily about getting students to “do history” in a modern way. Instead of requiring simple reading and writing about the past, students are asked to work with large amounts of data, find meaning in the data, use digital tools to display insights visually, and then share everything online. In short, Digital APUSH students are asked to act like 21st-century apprentice historians, examining the past quantitatively and taking research beyond textbooks, library books, periodicals, and traditional primary sources.

    • 2015 — Student work focused on text-mining the State and Provincial Papers of New Hampshire, which led to an analysis of anti-federalism within New Hampshire. 

    • 2017 — The project used more than 19,000 probate records to investigate life and life expectancy in colonial New England.

    • 2018 — Students text mined the papers of Thomas Jefferson and the tweets of Donald Trump in order to study issues and phrasing tendencies, and then tweeted as the 3rd president (@notrealTomJeff) in the words of the 45th (@realDonaldTrump).

    • 2019 — Data from newspapers.com were used to determine the most turbulent years in 20th-century United States history.

    • 2020 — The project examined the extent and tone of newspaper coverage in 1918 concerning the 1918 flu pandemic.

    • 2021 — Students looked for evidence of particular sentiments within numerous historical speeches.

    • 2022 — Students developed psychological profiles for songs and evaluated the profiles relative to their historical contexts.

    • 2023 — The project focused on building an election forecasting model for the NH primary.