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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

  • Parks and waterways
    • For almost every city map I've examined, the majority of urban parks fall along the waterways. There are many reasons for this pattern (flood zones, ecological protection, history of economic changes over time), but it seems remarkably consistent. 
    • Example #1, presented in a story map: Allentown Parks & Waterways
    • Example #2, presented in a sandbox ArcGIS Online map: Austin, TX parks & waterways
      • Note that I created multiple layers of the parks, filtered by different categories. This allows students to examine some of the discontinuities – cemeteries and golf courses, for example


  • Scaffolded geocache – teaches absolute location; can integrate computational thinking


  • "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

  • American social history via the names of public schools
    • The National Center for Education Statistics (NCES) keeps a searchable database of public schools. You can conduct thematic searches of this database, such as...
      • Presidents who have been impeached
        • Andrew Johnson = 1
        • Richard Nixon = 0
        • Bill Clinton = 3
      • Confederate leaders and generals (look for Jefferson Davis, Robert E. Lee, Stonewall Jackson, etc.)
      • Civil Rights icons (Martin Luther King, Rosa Parks, Bayard Rustin, Medgar Evers, etc. etc. etc.)
    • The relative proportions of the names tells you something about the popularity of these public figures.
    • The database also provides addresses, so you could map the results and explore the geographic patterns in their popularity. (Example #1: The lone school named after Andrew Johnson is in Kingsport, TN, about 30 miles from his residence in Greenville, TN. Example #2, far more puzzling: 'Stonewall Jackson' gives 7 results, all of which are in Virginia or Florida...with one in West Virginia.)



  • 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? 


  • 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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