by Natalie J.S-G.
Last updated on December 6th, 2022
for Data Visualization for Allwith Prof. Jack DoughertyTrinity College, Hartford CT, USA
Background
In exploring factors that impact a student’s journey through the public education system in the Greater Hartford region, it is crucial to examine the makeup of the student body. Specifically, I chose to delve into how many students enrolled in each type of public school are defined as ‘high needs’, in an effort to gain deeper insight into how much impact educational funding has on different student groups. “High needs” students are defined as students who are eligible for free and reduced lunch, students who are english language learners, and students with disabilities. I focused on the 33-town region encompassed by the term ‘Greater Hartford Area’, and the schools that are within this region, in order to provide a more focused frame for approaching this question. When examining how enrollment of students with high needs varies across districts in the Greater Hartford Area, we see stark differences between many district’s total number of students with high needs.
Terms Used
Throughout this data story, multiple terms are used to define key concepts. However, some of these terms are specific to this data itself, so definitions of terms are important to overall understanding.
- Students with High Needs: Students that are any combination of the following: English Language Learners, Eligible for Free and Reduced Lunch Plans, or Students with Disabilities
The interchangeable terms “Students with High Needs” and “High Needs Students” are both taken directly from the CT State Department of Education and do not accurately reflect a characterization of certain students that I would want to perpetuate. The current terms characterize students based on their needs level, whereas it is important to use people-first language so members of all groups feel better represented. For this reason, I introduce the term ‘Students Who Would Benefit from Additional Resources’, abbreviated for the purposes of clearer formatting as “SBAR” (or “SBAR’s “ in the plural form).
- Greater Hartford Area: For the purpose of this exploration, this area encompasses a 33-town area and its corresponding 33 school districts. Non-geographically based districts are not included in this data story. The 33 towns are as follows: Avon, Berlin, Bloomfield, Bolton, Canton, Coventry, Cromwell, East Granby, East Hartford, East Windsor, Ellington, Enfield, Farmington, Glastonbury, Granby, Hartford, Manchester, New Britain, Newington, Plainville, Portland, Rocky Hill, Simsbury, Somers, South Windsor, Southington, Suffield, Tolland, Vernon, West Hartford, Wethersfield, Windsor, and Windsor Locks.
What does the geography of where Students with High Needs are located show us?
In order to contextualize this question, I first used data from EdSight that I used to show the percentage of high needs students in each district, over the course of the 2021-2022 school year. The lighter areas represent districts where lower numbers of Students who would Benefit from Additional Resources (SBAR) are enrolled, and the color deepens as a district has a greater percentage of SBAR’s. These percentages come from the amount of students with high needs out of the total number of students enrolled, per each district, so that each percentage represents a number scaled appropriately for the size of the district.
In this chart, which was also assembled using student enrollment data from EdSight, we can see that the district with the highest percentage of students with high needs, Hartford, has nearly 4.5 times the amount that the district with the lowest percentage, Somers, has. This reinforces the idea that even within a relatively small-sized geographic area, there exists large disparities in where students with high needs are enrolled in schools.
What does the geography of where Students with High Needs are located show us?
In order to contextualize this question, I first used data from EdSight that I used to show the percentage of students who would benefit from additional resources in each district, over the course of the 2021-2022 school year. The lighter areas represent districts where lower numbers of students with high needs are enrolled, and the color deepens as a district has a greater percentage of students with high needs. These percentages come from the amount of SBAR's out of the total number of students enrolled, per each district, so that each percentage represents a number scaled appropriately for the size of the district.
As can be seen in Fig. 1 above, this map shows us that there is both a wide variance in percentages, and helps us contextualize a further dive into the trends that are present in Fig. 2. Additionally, with this map view of the data, it’s clear to see that there doesn’t appear to be a correlation between areas that are major population centers and the amount of SBAR's that are enrolled in those districts.
In order to construct the map (Fig. 1), I downloaded the ‘raw’ data for student enrollment (both total enrollment, and students with 'high needs' enrollment) from the Edsight database for CT school enrollment. I then opened the data in google docs, which I used to clean up the data and calculate the percentages of 'high needs' students and 'non-high needs' students so that the data that is shown in each visualization is normalized. I checked these values against the dashboard page of the EdSight page for the specific districts I was examining, and each value was validated by the percentages that EdSight provided. I then opened DataWrapper and selected a map that solely contained the 33-town districts that I chose to focus on, and uploaded the cleaned-up data and matched it so that each town/district had a value. I followed a similar process for the chart, and chose to use a 100% stacked type to show the data as that would account for needing to normalize the data so that districts that have much larger enrollments in total did not artificially inflate their amounts of students with 'high needs' relative to smaller districts.
It is important to keep in mind that the term high needs encompasses a large variety of student needs, so different factors might make up the total amount for each district. Because of this, it can be difficult to speculate on specific trends from district to district. When looking at the data as a whole, it is clear that not all districts have an equal distribution of the amount of students who would benefit from additional resources. There are great differences from highly concentrated districts to the not as highly concentrated ones. I believe that this data story provides foundational context for further exploration into how resources and funding are distributed across areas with more or less SBAR’s.