TEN Week: Translating Complex Models into Actionable Insight Across K–12 Nonprofit Evaluations by Angelicque Tucker Blackmon, Zhina Shen, Sydney Okland, and Katie Trautman

Date: Wednesday, February 25, 2026

Hi, we are Drs. Angelicque Tucker Blackmon, Zhina Shen, Sydney Okland, and Katie Trautman from the Division of Research and Evaluation at Innovative Learning Center LLC. As a team of evaluators working across STEM K-12 and higher education initiatives, we share a commitment to making complex data meaningful and accessible to the organizations we serve. Dr. Angelicque Tucker Blackmon and Dr. Zhina Shen bring decades of evaluation expertise and leadership in mixed-methods design and advanced statistical modeling; Sydney contributes strong expertise in quantitative data analysis and reporting; and Katie specializes in qualitative inquiry and translating findings into shareholder-ready insights. Together, we focus on how collaborative insights, augmented by data visualizations and structured interpretation, turn complex evaluation models and findings into actionable insights across K-12 nonprofit-based STEM programs.

Our team recently led evaluations of two K-12 nonprofit programs aimed at improving students’ educational outcomes outside traditional school settings. The first involved a STEM education program that provided over 175,000 4^th through 12^th grade students with hands-on science and engineering learning opportunities. The second involved a 21st Century Community Learning Center that provided after-school programming for middle and high school students. This evaluation examined a range of educational outcomes, including English Language Arts and math performance.

Hot Tips

In complex K-12 environments, we use data visualizations to reduce our clients’ cognitive load, communicate with diverse audiences, and deliver actionable insights.

Turning Data into Action

One of the most effective—and replicable—ways evaluators can increase the usefulness of findings is to translate complex data structures into clear, decision-oriented visuals and messages that non-technical stakeholders can immediately apply. Below is a concrete example that evaluators can adapt across K–12, higher education, and community-based initiatives.

An evaluation team collected open-ended data (e.g., focus group transcripts, implementation reflections, and stakeholder interviews) to understand why participation and outcomes varied across sites. Rather than stopping at thematic coding alone, the team built a Knowledge Graph by (1) coding text into a structured set of entities (e.g., “teacher coaching,” “student belonging,” “attendance barriers,” “family engagement,” “instructional time,” “career exposure”), and (2) explicitly mapping the relationships among those entities (e.g., “enables,” “constrains,” “predicts,” “co-occurs with”).

Lessons Learned

• Treat data visualization and interpretation as an ongoing, collaborative process rather than simply sharing data findings at the final reporting step to significantly improve clients’ understanding, trust, and ability to use findings generated through activities they’ve initiated. Subsequently, evaluation becomes more adaptive, more transparent, and far more useful for real-time program improvement.
• The interpretation of findings is substantially strengthened when it is carried out collaboratively with clients. This engagement allows them to contextualize results within their operational realities, institutional constraints, and strategic priorities—insights that are often not fully captured through quantitative or qualitative data alone. This collaborative interpretation process supports shared meaning-making, reduces the risk of misinterpretation, and increases stakeholders’ confidence in the validity and relevance of the findings.

Rad Resources

• Please read more on knowledge graph here.
• The ILC evaluation team will attend the 2026 TEN Virtual Institute where we will provide more details in our presentation titled “Curiosity in Action through Collaborative Data Visualization: Translating Complex Models into Actionable Insight Across K-12 Nonprofit Evaluations.”

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