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Nina Potter on Tableau for Data Visualization

My name is Nina Potter and I am currently the Director of Assessment for the College of Education at San Diego State University. I’d like to share a little about a tool we are using for data visualization.

One of my responsibilities is to work with program directors and department chairs to evaluate academic programs across the college’s eight departments and 30+ programs. Our programs vary greatly in size and each has its own goals and student learning outcomes. Plus, we have some common goals across the college. We wanted to have a common tool that would allow us to share data across the college, but it had to be very flexible in terms of the kinds of data that it could handle as well as the kinds of reports that it could generate. After a lot of exploring, we chose Tableau.

Rad Resource: Before coming to SDSU, I had never heard of Tableau, in fact I had not heard the term “data visualization tool.” First I will tell you what it is NOT. Tableau is not a tool for data entry. You use Tableau to access data from other data sources such as spreadsheets or databases. This was important because our programs use many different tools to collect data, from electronic portfolio systems to paper and pencil tracking (we do require them to at least put the data in a spreadsheet). And, Tableau does not do advanced statistics; although it does do simple regression and t-tests. For statistical tests, we still use other statistic packages.

So what does Tableau do? Tableau allows you to link into multiple data sources, and quickly and easily create interactive graphs and charts that are updated in real time as your data sources are updated. It has a variety of choices for visualizations such as tables, line graphs, bar charts, pie charts and geographical maps. With just a few clicks you can easily change the type of chart, add colors, add filters and drill down to data that fits certain criteria. The charts are interactive so that anyone viewing the charts can apply filters and view the data they want to focus on.

For example, we have some assessments that are given across multiple programs. We can create a chart that looks at student progress over time and add filters such as program, gender, ethnicity, and age. A person who is evaluating the program as a whole can compare the results from program X to program Y to see if there is equity across multiple demographic groups. Additionally, a person who is working with individual students can download a list of students who have failed more than one assessment in a given program.

Want to hear more about Tableau from Nina? Join her on April 29 for “Data in, Brilliance Out with Tableau” as part of AEA’s Coffee Break Demonstration Series. More information and registration may be found at Free for AEA members!

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