Photo: Katie Wang

On a warm March evening, after munching on a few slices of pizza and engaging in a lively session about the fundamentals of data and its applications, a teenage boy in Fifth Ward remarked, “It’s neat to see how we fit into statistics.” Now, he said, he understood better why so many organizations come through his school and after-school program to administer surveys, evaluations and assessments. “They’re using these to establish what they think we need,” he said.

Throughout the spring and summer, the Kinder Institute’s Houston Community Data Connections program and Rice University’s Fondren Library teamed up to conduct data literacy trainings in several of Houston’s underserved neighborhoods. With support from Civic Switchboard, a Pittsburgh-based project funded by the Institute of Museum and Library Services that encourages partnerships between libraries and local data intermediaries to fill the gaps in civic data ecosystems, we aimed to bolster data literacy, democratize data and support equitable access to data among Houstonians.

Data literacy equips people to understand the underlying principles and challenges of data and to use data in a way that supports their arguments or decision-making processes. While designing our data curriculum, we strived to empower participants to comprehend, interpret and use the data they encounter—and even to produce and analyze their own data.

We conducted three distinct trainings at the Urban Enrichment Institute (UEI) in Fifth Ward, Smith Neighborhood Library in Third Ward and the United Way of Greater Houston. By partnering with community organizations that are located in or work with high-need neighborhoods, our team gathered important information about participants’ unique social and cultural contexts. In so doing, we crafted curricula that was rooted in participants’ experiences and, as a result, more effectively forged personal connections and civic engagement through data.

UEI is a youth leadership and development nonprofit organization that serves at-risk male students in middle and high school. Our Student Teaching Fellow Daniel Koh, a rising senior at Rice, drew upon his experience and coursework in education to maximize engagement and real-world application for the student audience. He scaffolded the training’s various elements to make data literacy concepts learnable and relatable for participants with varying levels of familiarity with the subject.

We administered a pre-training questionnaire that strategically assessed participants’ knowledge of and interest in data. The results guided our creation of the conceptual portion of the training, inspiring us to focus largely on the fundamentals of data interpretation, visualization and potential pitfalls. Koh employed examples that were relevant to our young participants’ interests, including data showing NBA stars’ heights and player counts of the wildly popular video games Fortnite and Apex Legends.

A post-training assessment indicated that, on average, the UEI participants scored 34.5% higher on the quantitative section of the questionnaire after the training than before. To solidify this new learning, we left the students with a community data project they could complete independently. We showed participants how to access the HCDC Dashboard and investigate data about their own neighborhood. From there, the participants designed interview questions to collect qualitative data in dialogue with the quantitative data they discovered. The project gradually removed our team as the source of knowledge and gave participants the tools to tell their own stories through data. After completing their projects, students expressed feelings of empowerment and ownership of their personal and community data. One student stated, “The Rice training helped me understand how to ask questions about the information I am supposed to provide when someone asks me to be part of a survey, evaluation or assessment.”

The second training at Smith Neighborhood Library adapted the UEI pilot model to reach a more demographically diverse audience in a shorter time. We plugged into an existing after-school program facilitated by the library to recruit and engage community members. Because the Third Ward audience included adults as well as students, our examples of video games and sports from the first training were not universal enough.

In light of this, we incorporated data from the Third Ward directly into the conceptual portion of the training. Using Third Ward street safety data, we encouraged participants to think deeply about how data reflects and affects their lives in critical ways. For example, by exploring Third Ward sidewalk assessment data and maps, residents could visualize the way that pedestrians move about their neighborhood. After taking stock of the areas in which sidewalks are in good condition, seriously obstructed or absent altogether, residents could compare their own lived experience with the story the data told. 

Using data about Third Ward sidewalk conditions, training participants at Smith Neighborhood Library visualized the way pedestrians move about their neighborhood, taking stock of the areas in which sidewalks are in good or bad condition. Photo: Houston Community Data Connections

For our third and final Civic Switchboard training, we collaborated with the United Way of Greater Houston to reach nonprofit professionals in the fields of workforce development and financial coaching. We reflected on our learnings from the first two trainings and shaped them into a “train the trainers” workshop in which we discussed the purpose of data literacy, strategies and best practices, and examples of activities to conduct with clients that might successfully reinforce new concepts. Some key takeaways included:

  • Plug data literacy trainings into existing structures and/or programs with accountability mechanisms
  • Consider the audience’s context and experience when designing training components
  • Conduct pre-training data literacy assessments to determine the participants’ level of familiarity with data concepts
  • Set well-defined training goals
  • Be personal and vulnerable—explain how data affects YOU
  • Incorporate leave-behind projects and/or opportunities for implementing new knowledge
  • Conduct post-training surveys to gather feedback and discern the impact of the training

Central to the curriculum of each of our three trainings was the notion that the processes of data interpretation and collection are biased and inherently imperfect. We encouraged participants to ask themselves questions like, “Who has access to data? Who is telling stories with it? What stories are they choosing to tell, and why?” We attempted to make it clear to training participants that data literacy is not an end unto itself. Rather, it is a means toward a necessary reinvention of community engagement and empowerment, toward “data democratization” or “data inclusion.”

We encourage others to recreate our trainings in their own communities, adapting materials and frameworks to fit their unique social and cultural contexts. As we continue to re-work and strengthen our own curricula, we aim to participate in a wider dialogue about access to and inclusion in various data-related processes, and we encourage the communities we serve to do the same. 

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