Data Literacy
Exploring How Teens Co-Design After-School Programs
by Wenge Wang
About the Project
The goal of the Data Literacy with, for, and by Youth is to support data literacy
programs for youth at the library.
This project recruited 25 teenagers ages 13-17, including those from underrepresented groups, to co-design and implement four to six 90-minuten critical data literacy sessions in a public library.
My role is to create the data visualizations to summarize some of what the teens had to say about the data literacy
activities created and tested, alongside teen co-designers, during a series of Data Labs held at the
Brooklyn Public Library.
Date
May, 2023
Tools
Tableau, R, Excel, Adobe Illustrator, D3.js
What types of data literacy activities do teens engage the most?
With data from activity resources and exit surveys, I categorized the types of activities preferred by teens, and employed sentiment analysis to delve deeper. Teens rated a selection of activities on a scale of 1 to 5, with 5 representing the highest rank. The resulting chart presents the average scores for each activity and arranges their types in order. This methodology provides useful insights on the most popular activities among teens.
Click on Activities to See Ratings
Hover and See What Teen Participants Think ⤵
"I really like how each of us get to communicate with our peers and share our thoughts towards data literacy."
"I like making the story and trying to connected it back to data literacy"
"Everything was great and it never felt like a harsh environment and it felt relaxed. "
"I want you to know that I really liked this program and I had so much fun. Thanks for this great opportunity. "
"I liked the data, and doing our dream board. "
"I liked how we discussed different options for open data for teens and how we all pitched in our ideas. "
"I liked the jamboard session and the data quiz game!"
"I enjoyed brainstorming. "
“I would also suggest trying the squirrel activity outdoors and creating your own dataset similar to the squirrel data. This might also be helpful."
“I would have liked to explore my group's idea a bit more."
What Kind of Data did teens like to explore?
There were 24 Data Labs, divided into four different series.
The first two series dealt with personal digital data and concepts like privacy, metadata, and algorithmic
bias. The last two sessions were focused on civic data and community needs. They were designed to be more
practical and hands-on with data.
The chart displays the average rating for each series, indicating the teens’ overall perceptions.
Teens liked working with civic data more than concepts associated with personal digital data.
Overall, How Did Teens like the Labs?
Continuing Learning
Teens showed an 80% interest in further learning more about data.
Contribution
On average, teens rated their potential contribution to Data Labs at 80%.
Overall Interest
On average, teens rated their evel of interest in the Data. Labs overall at 90%
In These Areas, We Still Need to Improve:
In the exit survey dataset, I collected recommendations from teen participants and concluded that the main areas to improve are facilitation and content.
Data and Methdology
The dataset used in this project is from the exist surverys of four data labs in Data Literacy with, for, and by Youth, funded by the National Science Foundation (Award #2005608).
I used R and Excel to clean the survey data and conducted a comprehensive analysis of the teen participants' feedback regarding the data labs. This involved not only quantitative assessments but also sensitive analysis, digging deep into the specifics of their feedback to find meaningful insights.
After many collaborative sessions with Professor Bowler, I refined the final infographic, prioritizing user-friendly visualization. Through thorough discussion and iterative feedback, we ensured that the infographic not only accurately conveyed the data but also presented meaningful insights in a digestible format.
Takeaways
After iterating through multiple versions, I've realized that data visualization goes beyond showcasing numeric insights. It's more than just designers and analysts expressing themselves—it's about addressing meaningful research questions and goals. Audience understanding is crucial. Effective visualization connects with viewers by tailoring visuals to their perspective and needs. By considering both the audience and the clients, I ensure that the information resonates and communicates effectively. This iterative process emphasizes the importance of prioritizing people in data visualization.