Data Visualization Project - CHUNK 2

This is a continuation of last week's post on Data Visualization. You can find that post here. This week, I explored NYSED further and played with different ways of visualizing the data presented on their site. Last week, I mentioned that NYSED data was well visualized at https://data.nysed.gov. This is true for the NYS “At a Glance” or school enrollment in each district.

I noticed a gap, however, in visualization of graduation rates for demographics across counties and districts.

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Last week, I worked in Excel to compare my home district, Brewster Central School District, to my home county: Putnam. This compilation created a pretty interesting graph but I was still hoping for something more aesthetically pleasing and easier to interpret. Additionally, I wanted to add more districts and counties.

I never figured out the Visualize Free tool that I attempted last week, but Data Wrapper provided me with a lot of great options so decided to end my search for a data visualization tool. Once I started compiling data, everything began to look messy. I had to think strategically in order to visualize this data because there were a lot of competing variables clouding any possible conclusions.

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I decided to create 4 graphs in total. I decided to remove some data from the initial chart because to sharpen the impact of the visualization. The first chart is a compilation of graduation rates from NYS, Putnam County, Brewster Central School District, Westchester County, Bedford Central School District, and Scasrdale Union Free School District. I chose to compile these 6 data sets because I was involved in each of the aforementioned school districts at some point and the accompanying county/ state data is useful in painting a full picture of the surrounding districts.

Across the board, the English Language Learners had the lowest graduation rates. Out of all of the data represented, NYS had the lowest graduation rates in all categories except for ELL. This may be attributed to the fact that many ELL students need extra time to increase language acquisition of academic vocabulary, making it difficult to pass high school graduation requirements. However, the data presented on the NYSED website shows that most ELL students do not re-enroll in next school year.

Male students consistently graduated at a lower rate than female students. General education students graduated in higher rates than students with disabilities, and economically disadvantaged students were 10-29% less likely to graduate than their non-economically disadvantaged counterparts. Black/ African American students were 9-15% less likely to graduate than White or Asian/Native Hawaiian/Other Pacific Islander students. Hispanic/Latino students were 2-37% less likely to graduate than White or Asian/Native Hawaiian/Other Pacific Islander students in 2019. THIRTY. SEVEN. PERCENT. Does any of this sound, look, or feel like equitable education? The greatest discrepancy is seen between Hispanic/ Latino students and Asian/Native Hawaiian/Other Pacific Islander students in the Bedford Central School District. This data is particularly disheartening to me because the aforementioned schools recieve have greater funding than many other districts across the state. Even the whole of NYS, which is more economically diverse than Westchester or Putnam Counties, receives proportionally more funding than education departments in other states across the country. This entire situation reminds me of the storyboard below, created by Marcellus Mines. The NYSED statistics, if nothing else, remind us that we are presently in Box 3.

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After completing Figure 1, I decided to compile data from NYS and Westchester in order to create two additional charts. Figure 1 does not include data on the graduation rates of American Indian/ Alaskan Native students because the chosen districts did not have students of this background enrolled in the 2018-2019 graduating class. Figures 2 and 3 present data on the graduation rates of students of various racial and ethnic backgrounds enrolled across NYS and Westchester County. Both charts visualize the same data but it is represented in different ways. What do you think of this data? What do you think of these differing ways of visualization?

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Lastly, I chose to compile data representing student living situations. I did not include this data in the other graphs because NYSED only published this data for NYS and/or Westchester County. Putnam County and the three districts that I selected did not provide NYSED with data about student living situations.

To me, this data may be the most shocking. Homelessness, Foster Care, and Migrant living impact students more than any other demographic, aside from ELL. Figure 4 is a great reminder to treat all students with empathy and flexibility, as some of our students may experience unstable living conditions that hinder their academic success in our classrooms.