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A hasty list of topics and materials, organized by (approximate) grade level. Some of these are described in the publications we've been working on

  • Hammond, T.C., Oltman, J., & Salter, S. (2019). Using computational thinking to explore the past, present, and future. Social Education, 83, 118-122.
  • Hammond, T.C., Oltman, J.L., & Manfra, M.M. (In print). Computational thinking and social studies teacher education: What, why, and how. In S. Keengwe (Ed.), Handbook of research on integrating computer science and computational thinking in K-12 education. Hershey, PA: IGI.

All of them can be easily scaled up to be appropriate for older students.

In addition to these materials, please take a look at Esri's GeoInquiries for social studies (Government, US History, World History, World Geography, Human Geography) – they aren't explicitly framed as computational thinking activities, but they lend themselves to it readily.


Elementary-appropriate


  • "What's in a State Name?"


  • European settlement patterns in the Lehigh Valley (Note: This pattern repeats – or variations of it repeat – in many places)
    • Story Map presenting the relevant datasets, prompting student work, and posing key questions. It's not explicitly framed as computational thinking, but it's all there: dataset, pattern, rule


Middle level

  • Civil War battles in the Eastern Theater


  • Enslavement, emancipation, and the continuing struggle
    • Story of Aaron
    • 1860 census. (Note that if you progress from 1790 to 1860, additional patterns become visible – changes in intensity, geographic point of focus over time.)
    • Jim Crow laws
    • Lynching


High school


  • Jewish populations circa the Holocaust


  • Cuban Missile Crisis
    • ArcGIS Online map. This activity calls for measurement (proximity to capitals) and reference to primary sources.


  • Global terrorism, 1970-
    • ArcGIS Online map. This activity runs in all directions, really relies on filters. Opportunities to observe changes in time within and across different geo-political contexts. (Example: Northern Ireland pre/post 1998)


  • Material culture around the globe


  • Representation in Congress (no spatial element to it, or at least not that I've yet observed)
    • Women in the House of Representatives
    • LGBTQ members of the House of Representatives
      • Extract data from Wikipedia page, tabulate it by party affiliation (not enough data to be worth graphing), and observe the divergence circa 1996. What caused this? (Hint)


  • Contemporary American political polling
    • Start with a recent dataset from a single pollster, such as YouGov or Ipsos. Study the data structure
      • In what ways is this dataset an abstraction?
      • How has the pollster decomposed the problem?
      • What algorithm or rule is being used to generate the top-line result? 
      • What claim or generalization is being made?
    • Next, move to an aggregator – https://projects.fivethirtyeight.com/trump-approval-ratings seems to have a very clear, accessible layout. 
      • Understand the new, more complex dataset (weights, adjustments, etc.)
      • Look for patterns across pollsters (LV vs. RV vs. A screens, for example)
      • Generate rules or predictions – what will things look like 2 weeks from now? Why? 
    • To turn to a meta-discussion of computational thinking: The aggregator is an abstraction of a set of abstractions. Does this make it more accurate / informative / reliable? If so, why? How does that work? 



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