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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.


  • "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 – 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? 

  • For computer programming classes: Google and Bing now recognize geo-coordinates as a data type. For example: "40 N, 75 W" produces a map as its first search result – it's just across the river from Philadelphia. This means you can write Python scripts that automate one or more geo-searches. Some possibilities:
    • Given a list of class birthdays, generate a set of maps that show everyone's "birthday location". Example: Someone born on 12/12 would have a birthday location in northeastern Nigeria (12 N, 12 E)
    • Given coordinates for a location (say, a student's home address), generate a map of the lat/lon on the opposite side of the world. (For example, San Diego's lat/lon is approximately 33 N, 117 W. The opposite lat/lon (33 S, 117 E) is approximately Perth, Australia). You can decompose this task by first getting the mirror lat (33 S, 117 W) and/or mirror lon (33 N, 117 E). You can add complexity by indexing the resulting lat-lon to a table of world-wide cities and automatically generating the closest major city. 
    • If you can write scripts for Google Earth, see if you can do the same thing for Mars or the Moon. (For example, the Opportunity Rover is at about 2 S, 5.5 W...and it's not moving from there.)

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