Seminar Redesign

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1) Reduce and insert data viz as a 3-week module into a topical seminar, similar to how I taught an abbreviated version during Ed 308 Cities Suburbs Schools seminar in Fall 2013

  • The students get experience with data visualization


  • The experience the students get doesn’t get to get developed and built upon as much as it would if it were a full semester course

2) Keep as-is with current 0.5 credit seminar combined with 0.5 credit internship.

  • It’s good to get internship experience with a mentor and build a semester long relationship
  • Ability to get fairly extensive data visualization instruction, but still be able to apply it to a community partner internship


  • It isn’t an entirely in depth instruction of data visualization, for example we don’t learn how to code

3) Expand into a larger 1-credit course, with more data viz content and coding instruction during the first half, and pairs of students working on community partner projects during the second half. Also, may count toward Numerical & Symbolic reasoning requirement.

  • Get more focus on the data visualization which can help further understand it
  • Could lead to an ability to do more advanced visualizations 
  • Get numerical & symbolic reasoning credit for it


  • Don’t get the full internship experience, particularly of building a relationship with a community partner throughout the whole semester

Recommendation: My preferred option of these three would to be to stay with the seminar as it is now with it being half credit seminar and half credit internship. I found the internship experience INCREDIBLY valuable, so I think that should definitely remain part of the course.

MoveUP! Writing about Data

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Demographics can play a large role in literacy, and what MoveUP! wants to try to figure out is what types of demographics are making an effect on their students. If certain factors make a large enough impact, it could potentially be something that MoveUP! would want to take into consideration when they advertise their adult literacy programs.

This graph shows the median household income per each census tract in Hartford County. It is evident that the western part of Hartford county has a higher median income, whereas the actual city of Hartford seems to have the lowest. When you click on the tracts, though, you are able to see that many more people are attending adult literacy programs in the tracts that are in the city of Hartford in comparison to those that are in towns that have higher median incomes such as West Hartford.

Building upon the first map of income, this map shows the distribution of the poverty status for individuals 18-64 years of age within the various census tracts of Hartford County. This map essentially shows the inverse of what the previous map showed: it accentuates the lowest income areas, which is, again, primarily the city of Hartford.

Another demographic that isn’t related to income that is important to look at, particularly in relation to literacy is employment. There is typically a strong correlation between employability and literacy skills, therefore it is important to look at what kinds of match ups there are in terms of unemployment. What is essential to note about what is considered to be “unemployed” in this particular map and data set is that those individuals are part of the labor force. There are also individuals included in the data set that are under the category of “not in labor force”. It is interesting to note, however, that the highest rate of unemployment isn’t necessarily in the same tracts as the lowest income.

MoveUP! Lying with Maps

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This first visualization is a gradient polygon map of the poverty status for individuals 18-64 years of age within the various census tracts of Hartford County. The goal here was to make each tract look very similar data-wise, which is why each tract is a very similar shade green. The map is showing that throughout the county, and within each tract, there aren’t many individuals living in poverty. It does show, though, where the higher concentration of poverty is, so it doesn’t hide it.


The second visualization is a two-bucket polygon map of the poverty status for individuals 18-64 years of age within the various census tracts of Hartford County. My goal was to show an extreme difference in data in this particular visualization, which is why I used the two-bucket technique. I found a number boundary that accentuated how many individuals were in poverty and where they were primarily located.