Improving Student Outcomes: Visualizing School Data with Tableau

Preparing kids for the future of our world and the work force is an essential goal for schools. Understanding how students are performing in school, even as young as 4th grade, and looking at what these students are choosing to do after graduating high school informs us on how our schools are supporting their growth and preparation for the world beyond education.

Massachusetts schools are working hard to prepare students for their futures. Taking a closer look at how 4th graders are performing on standardized assessments and analyzing the impacts of class size, economic disadvantages, and gender gives us some insight on how elementary school progress may affect graduation rate and choices after high school.

What did we learn?

-Elementary schools are showing strong academic performance in reading and writing, however, majority of schools are not performing at the standard level when it comes to math.

-Graduation rates are consistently high for many districts.

-As you may expect, economic disadvantage of the school district does impact performance on these standardized assessments.

-Surprisingly, class sizes didn't impact graduation rate all too much. However, gender and students with diagnoses that impact their learning in the classroom (the "high needs" learner category) do show a direct impact on graduation rate.

-Finally, 75% of students are attending college after high school. The remaining students are enrolled in community college, in a 2- or 4- year program, or have dropped out. More data is needed to learn about the choices of work and education the students are making beyond high school.

You can visit the full Tableau Dashboard to view the analysis.

About the data

The authentic, open-source data comes from a fictional Massachusetts governor, who requested to be given insight into which schools aren't doing well in their graduation numbers as well as explore factors that could impact this number including elementary test scores as an early indicator of success.?

This particular data set has one table with 298 fields and 1860 records. It contained information about testing scores, gender and race distribution, graduation rates, class sizes, school and district names, and more. It came as an Excel CSV file.

Exploration

Diving into the data I saw the schools within each district, their assessment scores for each grade level as well as information on the student population (i.e. gender, race, learning disabilities, etc.).

I was at first concerned about the number of null entries throughout the data set, but upon further exploration I could detect that they were intentional nulls, showing the field didn’t apply to that particular school. For example, each of the 4th grade assessment data fields only had data for the elementary schools and not for the middle or high schools, as would be appropriate.

There were no duplicates. I adjusted all data types for each field. No additional calculated fields were necessary prior to loading the data into Tableau.

Analysis

First, to help the governor get an idea of how the high schools were doing with their graduation rates throughout the state, I created a couple bar graphs for comparison. Due to the names of the schools (charter schools often don't have "high" in their name, but instead "prepatory"), I broke this up into “public” schools and “charter” schools in order to analyze all high schools. The state governor was particularly concerned with the schools with lowest graduation rate. Out of the 1,861 schools in Massachusetts, here are the bottom 3 in terms of graduation.

I was curious about the factors that could impact graduation rates, so making a scatter plot with class size helped to see if there was any correlation between these two factors. The scatter plot surprisingly didn't show a strong relationship between a small class size and higher graduation rate, as you may expect it would, which is still good information to note. Based on this result, I wouldn't recommend to the governor the necessity to build additional schools at this time (as this would have helped to reduce class sizes).

This led me to thinking about other factors that might actually influence graduation rate, so I created additional scatter plots mapping out gender's relationship and "high needs" learners' relationship to graduation rate. These had a stronger correlation, where females had a higher graduation rate than males and "high needs" learners had a low graduation rate compared to other leaners who don't have learning disabilities or diagnoses.?This information is useful in a broad understanding at the state level, but also at the district and school level to continue to develop strategies to support all learners.

Overall, out of the 953,748 students, about 83% of students are currently graduating high school.

Considering elementary school data is an important indicator of student progress toward successful graduation, I then analyzed the 4th grade Math and English (which consists of Reading and Writing subject areas) assessment data. It was impressive to see that over 70 schools surpassed or met the norm passing score in English. But it was disappointing to see only 4 schools met this criteria for passing the math assessment.

An additional piece to consider is the economic disadvantages of school districts throughout the state and the relationship this has on test scores. The map here shows how the districts with less economic advantages also have lower 4th grade math test scores, as you may predict.

Next Steps

Based on the analysis above, I would recommend:

-Instead of considering class size reduction by building more schools, I would instead focus on the economically disadvantaged school districts, finding ways to better support them. More information would be needed to understand what they need but this could include actions like adding free-lunches to these schools and raising the budget for necessary school supplies.

-Reach out to the math teachers in the highest scoring school districts to learn about their effective methods for teaching math, as this can help advise plans for improving 4th Math Passing Scores across the state: Community Day Charter Public School : R. Kingman Webster (District), Community Day Charter Public School : Prospect (District), Community Day Charter Public School : Gateway (District), and Orleans.

This analysis was created by Ashley Zacharias

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