The purpose of this dashboard is to explore the data of Virginia public schools and identify how inequality, like in school or city funding, impacts performance in school. First, learn more about the problem in the About Tab, then explore the dataset yourself in the Explore Tab. Consider the following research questions while using the dashboard:

  1. How has school performance changed over time?

  2. How do differences in school funding correlate to overall school performance?

  3. How does location impact schools?

  4. How do schools impact their neighboring schools?

Dataset Overview

We combined 4 datasets on pre-K to 12 public schools in Virginia published by the University Libraries at Virginia Tech. Each dataset includes about 2,000 schools and the following features:

  • Standardized test scores from 2012-2017 in English, Math, History, and Science

  • Number of students with free or reduced lunched from 2008-2017

  • Number of students per grade from 2008-2017

  • Coordinates of each school

An Introduction to Virginia Public Schools

In this section, learn about our dataset and the structure of Virginia Public Schools.

Insight: An overview of Virginia regions. Data from the UVA Weldon Cooper Center for Public Service.

Insight: Most public schools in Virginia are elementary schools (preK-5). Elementary schools in a given area are often much smaller than neighboring middle and high schools.The number of middle and high schools in Virginia are both about 300.

Insight: Accreditation status varies by school level. Middle schools are the least fully accredited overall, even though there are fewer middle schools throughout the state. High schools, despite having the most rigorous curriculum out of the three, maintain high full accreditation levels.

Viewing the accreditation status of schools broken up by level acts as an overview for overall school performance across Virginia. A majority of schools maintain a fully accredited status, hovering around 80% for all levels of schooling. The percentage of partially accredited schools is about 20% across all levels. Finally, the percentage of schools who were denied accreditation is overwhelmingly small, with it being close to only 3%. This chart shows that schools in Virginia have differences in there performance in all levels of schooling.

Insight: This chart shows that the distribution of free and reduced lunch percentage varies among the different schools in Virginia. Notice that it appears normalized to about 50%.

Inequality and School Performance

In this section, examine how school performance is impacted by various metrics of inequality, like percentage of free or reduced lunches at a school or the region of the school. School performance is measured by accreditation, which is granted to schools by the Virginia Department of Education following several guidelines, and Standard of Learning (SOL) test scores. SOLs are standardized assessments across the state of Virginia measuring student mastery of key academic standards in the subjects Math, Science, English, and History.

Insight: Averaged across all subjects, more affluent areas of VA tend to have higher SOL pass rates (e.g., Northern VA is 2-5% higher than the next highest scoring regions, Central and Eastern VA). Pass rates tend to follow a consistent ranking: History, English, Science, Math (from highest to lowest). Mean SOL pass rates appear to have dipped significantly across all regions & school types between 2012-13 and 2013-14, but this may be due to inconsistency in the dataset; data for 2012-13 was sparse and disorganized.

Insight: Within a similar geographic region, test scores are often separated by county lines and clear clusters of high scores and low scores can be observed. For example, in Central VA, Chesterfield and Henrico County (west) have much higher scores than Richmond City and Henrico County (east) despite being geographically close. A similar county divide can be seen in Hampton Roads between Newport News City and York County. The counties or cities with low or high scores correlate with low or high income areas.

Next Steps

Now that you have an idea about our dataset and the relationship between inequality and school performance in Virginia Public Schools, click on the Explore Tab at the top of the dashboard to investigate more.

In this section, explore the dataset using interactive visualizations. Use the research questions and insights to guide your exploration.

Question 1: How has school performance changed over time?

This interactive line chart shows the mean SOL pass rate for a selected subject, separated by demographic region. Subjects can be selected with the dropdown in the top left, and the visibility of regions can be toggled by clicking on them in the legend.

Notice that:

  • Rankings between regions are roughly consistent across all subjects, especially in English.

  • There is a large shift in Math rankings between the 2012-13 and 2013-14 school years. Additionally, science pass rates dipped greatly following the 2012-13 school year for all regions and stayed relatively unchanged in the following years.

    • These trends may be attributed to low-quality data in the 2012-13 school year.

  • History pass rates are tightly grouped together, aside from Eastern and Northern Virginia, which are about 2-4% lower and higher than other regions, respectively.

    • This low variance in pass rates compared to other subjects may be caused by a smaller difference in teaching styles across regions.

    • While the English, Math, and Science SOLs focused heavily on testing understanding of subject matter, the History SOL could be seen as relying disproportionately on rote memorization.

  • Across all subjects, Northern Virginia schools consistently perform better than other regions, although the gap is smaller in Science pass rates.

    • This can likely be attributed to differences in funding between school districts.
Question 2: How do differences in school funding correlate to overall school performance?

In this graph free/reduced meals and average test scores are used as a proxy for student poverty rates and school performance. Notice that the downward trend of the data is constant even while filtering for school type. Also notice that a majority of schools with less than full accreditation are collected on the right side of the graph, indicating a higher free/reduced meal percentage, and therefore a higher likelihood of the students of that school experiencing poverty.

Question 3: How do schools vary by location?

This interactive chart allows the user to explore key features of our data set in aggregate. Hovering over the map provides access to location-specific figures, and annotations of major cities makes this hover navigation easier. If the user does not know where a particular county is or is drawn to the accreditation bars, they may select a bar and the corresponding county will be highlighted on the map.

Question 4: How do schools impact their neighbors?

This visualization shows the similarities and differences between test scores among geographically close schools. Use the buttons to choose between SOL subjects and the slider to select school years. Hover on points on the map to get individual information for each school, and use mouse click and scroll to pan and zoom. Notice that the highest test scores are clustered in the Northern region of Virginia.