Career and technical education (CTE) programs continue to evolve, offering students more meaningful ways to connect their academic experience to future careers. Today’s CTE programs are designed to prepare students for high-wage, high-demand industries by combining academics with work-based learning experiences. But connecting students to jobs takes more than strong program design—it requires clear goals, strong partnerships, and a commitment to continuous learning and program refinement.

MDRC works with school districts, nonprofit organizations, and funders to help them use data to learn about and improve their programs. Drawing on extensive expertise in evaluation research and technical assistance, MDRC helps its partners build systems and routines, refine program implementation, track progress, and strengthen program delivery by taking the following steps.

1. Clarifying Goals and Building Frameworks

MDRC begins by helping partners articulate their program’s goals and underlying assumptions about how specific activities (for example, mentoring, internships, training) will directly influence short- and long-term outcomes. This step often involves codeveloping a theory of change—which maps the causal pathways and assumptions behind the program—or a logic model, which outlines the program’s inputs, activities, outputs, and outcomes in a structured format. Developing this framework is more than a conceptual exercise: It anchors the work, guides strategy, and defines what should be measured.

For example, JPMorgan Chase provides grant funding to help nonprofit organizations across the country expand the programming and opportunities that are available through summer youth employment programs (SYEPs) and focus on providing high-quality program experiences that help connect young people to careers. As the learning and evaluation partner, MDRC collaborated with the foundation to develop a logic model that outlined the services that are offered through SYEPs—such as mentoring and skill-building opportunities—and how these activities help promote young adults’ career exposure, skill development, and long-term economic mobility.

Next, MDRC developed a common measurement framework that would be used by 30 grantee programs. The framework standardized the way programs measured key components of the logic model (like employer engagement) as well as participants’ internship placements, access to career readiness support services, and exposure to high-demand industries. By grounding the framework in the logic model, MDRC ensured the data reflected the programs’ goals, supported consistency and comparability across programs, and enabled the foundation to assess progress toward its intended outcomes.

2. Strengthening Data Collection Practices

Once a guiding logic model is in place and metrics have been defined, the next step is building the infrastructure to collect the right data at the right time. MDRC helps partners strengthen how they collect, organize, and use data by designing tools—such as data templates, surveys, interview protocols, and performance dashboards—and embedding them into existing routines.

One example is MDRC’s work with the Kauffman Foundation’s Real World Learning initiative, which aims to ensure all high school graduates in the Kansas City region leave school with at least one “market value asset”—such as an internship, credential, entrepreneurial experience, or college credit—that prepares them for future opportunities. As the learning partner, MDRC developed standardized templates to help over 30 participating school districts consistently track student attainment of these assets.

MDRC partnered with district teams to adapt the templates so they could be integrated into existing data systems and aligned with routine reporting processes in each district. This approach built on what the districts were already doing, making it easier to capture consistent information without overburdening staff members. In turn, it strengthened local capabilities and created a foundation for shared learning across the region.

3. Turning Insight into Action

With strong data systems in place, the next step is making that information usable. It is not enough to collect data—organizations need dedicated time and processes to reflect on what the data shows, interpret patterns, and consider how that information can inform practice. Otherwise data often goes unused or is applied inconsistently. MDRC supports this process not only by creating space for reflection—through feedback memos, learning sessions, and structured discussions—but also by working directly with partners to analyze and interpret the data and helping them translate findings into actionable strategies.

Through the Real World Learning initiative, MDRC has supported leaders in reviewing data that highlights how students’ access to and participation in experiences that produce market value assets varies across schools and student groups. These reviews raised important questions about equity and consistency: Which experiences were most common, and in which districts? To what extent were opportunities to pursue market value assets promoted and accessed equitably? As a result, some districts reconsidered how they promoted opportunities to students, strengthened partnerships with employers, or developed and tested new approaches to expanding participation to include students from families with low incomes.

In New Orleans, MDRC serves as an evaluation partner to YouthForce NOLA, a citywide initiative that connects high school students to careers through internships and training. As part of this work, MDRC collected and analyzed data on internship participation—and conducted an analysis that disaggregated data by gender. After discovering that young women were underrepresented in tech and skilled-trade internships and young men were underrepresented in health-related internships, YouthForce NOLA developed targeted outreach and recruitment approaches to encourage more young women to explore tech and skilled trades and to engage more young men in health-related opportunities.

Both examples show that it is important to reflect on what data reveal, engage in discussion with program leaders, and act on shared insight in order to turn information into meaningful improvement.

Conclusion

CTE programs become more effective through continuous learning and adaptation. Designing a strong program starts with a clear model, but it is the ongoing use of data and feedback that drives real improvement. MDRC helps its partners build the systems and routines they need to clarify their goals, improve program implementation, and respond to their clients’ changing needs—so programs can better support students and grow stronger over time.