A Bottoms-up Approach to Data: How We Leverage Our Diversity to Better Understand NYC Students’ SEL

Diversity is our strength

As a network, we believe that the solution is often in the room. Our role is not to have all the answers, but rather to facilitate data-informed learning and collaboration among practitioners to serve all students better. Our members are trying to answer big questions through our social-emotional learning (SEL) data collection and continuous improvement work. Which SEL competencies should practitioners focus on, when, and with whom? Which practices drive SEL growth for young people? In this space of questions and possibility, the diversity of our member organizations’ approaches to serving young people is a strength that allows us to get richer answers. A diverse network promotes creativity and speeds up problem-solving by exposing practitioners to different approaches for meeting student needs, shared challenges.

Practitioners recognize the potential of a diverse network and have asked us to facilitate SEL data comparison, particularly among programs with similar approaches to serving youth. This data-sharing would help identify best practices to eventually disseminate network-wide. The task of grouping similar programs seems simple in theory, but is challenging in practice. Categorizing programs by the services they offer is difficult because each program creates a unique and evolving blend of services. How can we decide if a program is “sports” or “college prep” when both are equally important to the organization’s identity? Perhaps we could use a more clear-cut variable like age range, neighborhood, or program size. However, these categories leave out important aspects of program design, and many programs do not serve students in just one age range or neighborhood.

“In this space of questions and possibility, the diversity of our member organizations’ approaches to serving young people is a strength that allows us to get richer answers.”

As a more rigorous way to group similar programs, the SSN data team, in collaboration with the Research Alliance for NYC Schools, created multiple, overlapping categories that encompass the services that programs provide, the populations they serve, and program size. We designed the categories and sorted programs into them using publicly-available materials (such as websites and applications), planning forms that staff submitted to SSN, and student self-report survey responses (for gender and grade). At the end of this post, you can interact with our baseline data to highlight members of different categories. We’d love your feedback on the definitions of the categories and, if you’re a member, on the ways we categorized your program.

Graphic of program categories including academic, arts, career, college prep, leadership, mentoring, sports, and STEM

Categorization May Reveal Meaningful Trends

The baseline survey speaks to students’ SEL skills coming into a program, which, unlike skill growth, may seem out of a program’s control. How can a program influence or anticipate certain skill sets even before the program begins? An application or interview process can help practitioners get a sense of whom they will serve before meeting students, but other decisions and program offerings can also serve to intentionally or unintentionally select for young people with particular SEL levels. For example, programs who serve college bound students might anticipate skills that have lent themselves to academic success in the past. Or, a program located in a tight-knit community may serve students with a high sense of belonging.

Refining these program categories with your input can help the network make connections between program models and incoming SEL skills or skill growth over time. For example, if you highlight the arts programs in the chart below, you will see that these programs happen to serve students with greater SEL needs on average. To figure out why, we’ll need to gather more information from you and explore the effect that art programming, or any category of programming, may have on students’ incoming SEL, as well as analyze and share strategies for improving SEL within a specialized program context.

As we refine the framework, we’ll share out meaningful trends within particular groupings of member organizations, but also need you to guide our research by sharing your theories on why some of these surprising trends might exist.

Explore the network-wide baseline data

Curious about our member programs? Highlight groups to see how the incoming SEL results of programs within that group compare to the network. Watch how the average line shifts as you highlight different groups of programs.

  • What do you notice about different groups?
  • Are there any trends you think you can explain?
  • Are these categories defined clearly? Are they important for understanding youth program types? Would you add or remove any categories?
  • Was anything surprising?
  • What trends would you like to explore further?

 

Share your thoughts with us in the comments!

Thank you to our collaborators:

Corall Azouri
M.Ed. Student at the Department of Education Policy and Social Analysis at Teachers College, Columbia University

Kavya Beheraj
Data Manager, Student Success Network

Sophie McGuinness
Research Analyst, Research Alliance for NYC Schools

Learn more about our SEL survey measures and questions here. If you are a member of the network and would like to know your program ID, reach out to Kavya Beheraj at kavya@ssn-nyc.org.

Related Posts:

Alexandra Lotero
alexandra@ssn-nyc.org
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