Using AI for Data Visualization of Educational Proficiency Rates

Overview

This use case demonstrates an iterative approach to data visualization using AI, specifically focusing on analyzing math proficiency rate trends between district and state levels. The workflow shows how breaking down complex data analysis tasks into smaller, sequential prompts can be more effective than using a single comprehensive prompt.

Prompt Used

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1. "Let's use the data in the spreadsheet to make a series of charts. Let me know when you have reviewed the file and are ready to begin."

2. "You will notice some blank cells for assessments in the year 2020. These are important, and we will have a solution for them later."

3. "We'll start with math proficiency rates. In each case, we will make a line graph allowing us to compare the district with the state. Use a blue line for District and a green line for the state. Place the year on the x-axis and the graduation rate on the y-axis. Include a title and key."

4. "Very good. Let's make an adjustment for the missing 2020 data. Can you add a dashed line connecting the 2019 and 2021 data?"

Other Content Provided

• Proficiency rate data from Washington's Data Portal

Additional Information

The user noted that this iterative approach to data analysis through smaller, focused prompts proved more effective than using single detailed prompts. This method particularly showed advantages when:

• Making adjustments to visualizations
• Repeating similar processes for different data sets
• Handling missing data (such as the 2020 gap due to pandemic interruption)
• Managing changes in test blueprints The approach helped facilitate meaningful discussions about resource needs despite gaps in state report card data.