A Below, find 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. (2019). Bridging STEM and the civic mission of social studies: Integrating spatial reasoning & computational thinking into decision-focused secondary social studies instruction. In D. Gibson & M. Ochoa (Eds.), Research highlights in technology and teacher education 2019 (pp. 127-132). Waynesville, NC: Association for the Advancement of Computing in Education. (proceedings page)
- Hammond, T.C., Oltman, J., & Salter, S. (2019). Using computational thinking to explore the past, present, and future. Social Education, 83, 118-122. (article available)
- Hammond, T.C., Oltman, J.L., & Manfra, M.M. (In print2020). 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 (pp. 1-16). Hershey, PA: IGI. (publisher site)
Manfra, M.M., Hammond, T.C., & Coven, R.M. (2021). Assessing computational thinking in the social studies. Theory & Research in Social Education. DOI: 10.1080/00933104.2021.2003276 (publisher site)
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
- Slideshow about the concept
- Sample data sheet of targets
- Useful app for providing lat/lon coordinates, if you don't have dedicated GPS units: My GPS Coordinates (Android version, iOS version)
...
- Enslavement, emancipation, and the continuing struggle
- Story of Aaron
- The centerpiece is a set of six advertisements from the Geography of Slavery database.
- These work much better as a powerpoint presentation (write to me for a copy; I got it from Stephanie Van Hover at UVA)
- 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
- ArcGIS Online map. This activity relies heavily on filters.
- Lynching
- Story of Aaron
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.)
- Presidents who have been impeached
- 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.)
- The National Center for Education Statistics (NCES) keeps a searchable database of public schools. You can conduct thematic searches of this database, such as...
- Correspondence with FDR
- ArcGIS Online map. This dataset is drawn from the work of Christopher Brockman, doctoral student in History at Lehigh University
...
- Representation in Congress (no spatial element to it, or at least not that I've yet observed)
- Women in the House of Representatives
- Review data on the Wikipedia page, specifically the graph by party affiliation, and observe the divergence circa 1992. What caused this? (Hint – among other factors)
- 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)
- Women in the House of Representatives
- 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?
- Start with a recent dataset from a single pollster, such as YouGov or Ipsos. Study the data structure
- 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.)
A counter-example: Cellphone towers and COVID-19 – this is an attempt to demonstrate that not all patterns/rules are equal.... Has a point layer for cellular towers, a clustered version of that layer, and then clustered county-level data or COVID cases for April, August, and November of 2020.