The role of education extends far beyond textbooks and classroom walls. Educators can ignite students’ curiosity about the world, foster their personal growth, and equip them with the skills, knowledge, and confidence they need to thrive after graduation. Schools play a central role in preparing high school students for college and careers, and students are more likely to benefit when this programming is intentional and holistic. To move purposefully toward these goals, school and district leaders should regularly use data to help inform decisionmaking. They also benefit from developing and using a logic model that functions as a road map and facilitates effective college- and career-readiness planning, implementation, and evaluation.
Data-to-Improve project
Regional Educational Laboratory Appalachia (REL AP) partnered with the Niswonger Foundation and ten school districts in northeastern Tennessee to support students’ preparation for college and careers. To improve readiness outcomes and build educators’ capacity to support students’ learning toward these objectives, the partnership initiated the Data-to-Improve project. The project’s goal was to enable districts to use data to strengthen college and career readiness through improved programs, policies, and practices.
The collaboration resulted in a co-designed set of training materials that have been published and made generalizable for local education leaders interested in building district capacity to use data to enhance college-and career-readiness preparation in their own communities.
Four pillars of support for college and career readiness
The partnership identified four interconnected areas that district leaders can attend to as they support high school students’ preparation for college and careers: academic, social-emotional, financial, and logistical preparation.1,2 As depicted in the infographic (figure 1), the pillars represent the need for rigorous coursework, confidence-building and mentoring, clear guidance on financial planning and management, and help completing the practical steps required to secure employment or enter postsecondary institutions. Together, they offer a comprehensive and holistic approach to supporting college and career readiness.
Developing a logic model: A road map for your district or school
A logic model is a visual diagram or road map illustrating how something works by connecting efforts to achieve desired results.3 As described in the training materials, a logic model is made up of key, interrelated components intended to lead to meaningful change. These parts form a road map, linking inputs (e.g., staffing, funding, materials) to processes and activities (e.g., programs, policies, practices) to outputs (e.g., tangible results, measurable things people create or do) and ultimately to outcomes (e.g., attitudes, skills, behaviors).
The partnership took a systematic approach in organizing its logic model around the four pillars of college and career readiness, as illustrated in figure 2.
“A program logic model is a picture of how your program works – the theory and assumptions underlying the program.... This model provides a road map of your program, highlighting how it is expected to work, what activities need to come before others, and how desired outcomes are achieved.”
– W. K. Kellogg Foundation Handbook (1998)
As with the partnership, strengthening your school or district’s system of supports for college and career readiness requires intentionality through data use. You can begin your efforts to use data effectively by developing a logic model that articulates how activities, practices, and resources are intended to lead to desired outcomes for your students. This logic model then serves as a foundation for developing meaningful research questions to help you examine what data you already have, what data are missing, and what additional data need to be collected.
Brainstorming research questions
With this logic model in hand, the partnership members reflected on what they wanted to know related to each pillar of college and career readiness. They used the linkages in their logic model, including any gaps that surfaced, to develop their research questions, including:
- For academic preparation
- Are our students academically well prepared and well trained to meet local job/industry needs and regional economic development plans?
- For social-emotional preparation
- How can core content teachers integrate social-emotional preparation in their instructional practices?
- For financial preparation
- What systematic supports can district/school staff provide to meet the financial preparation needs of all students, including those who do not plan to go to college directly from high school?
- For logistical preparation
- Are there differences in the groups of students who choose to pursue career awareness, exploration, preparation, and training opportunities?
These research questions helped partnership members better understand whether what was being implemented in their district was having the desired outcome and where they could identify points of leverage to improve their efforts.
Collecting and analyzing data that shape your next moves
To deepen their understanding of different types of data and data analyses relevant to their logic model, the partnership relied on Safir and Dugan’s framework of data use in education to guide their next steps (figure 3).[i] For example, when assessing how one of their outputs (e.g., educators will engage each student to create trusting relationships) would lead to a short-term outcome (e.g., students will develop self-efficacy and goal-setting), they recognized that to understand students’ experiences it was necessary to go beyond the traditional metrics of test scores and grades, which only tell part of the story. They saw the value in expanding their data collection to include student voices and staff perspectives through the use of empathy interviews and formative assessments. This ultimately reflected a mixed methods approach in collecting and analyzing both quantitative and qualitative data.
In the same way, your logic model helps you to stay on track with your goals, pointing you to the appropriate level of data collection necessary to measure the outcomes you’re hoping to achieve. These levels are described further below: 4
Figure 3: Satellite data, map data, and street data (Shane & Safir, 2021)
- Satellite data show us trends in large brushstrokes to point us in a general direction of an improvement area. They are summative data, such as graduation rates and test scores, that can be used awareness but can lack context and nuance.
- Map data give us more detail and help us to identify skills gaps. They are formative data, such as grades, that can be used for agency, giving teachers further direction about how to improve their instructional practices.
- Street data are fine-grained and focus on how things are experienced to understand why there are gaps. They can be used for action because they reflect the voices and experiences of students, staff, and families. The goal of street data is to help you improve your practice by seeing the full context of the learning experience.
Building your road map for data informed decisionmaking
While data play a critical role, it is the strategic use of data to inform decisionmaking that is a precondition for program improvement. By using data and developing a logic model as a road map, district leaders can be more intentional in their planning, implementation, and evaluation of their college and career preparation efforts. The materials produced through this partnership can help district leaders and staff to collect, analyze, and use data in a systematic way to improve efforts to prepare their students for life after high school. Access the materials here.
1 Tierney, W. G., Bailey, T., Constantine, J., Finkelstein, N., & Hurd, N. F. (2009). Helping students navigate the path to college: What high schools can do (NCEE 2009-4066). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. https://ies.ed.gov/ncee/wwc/PracticeGuide/1
2Rumberger, R. W., Addis, H., Allensworth, E., Balfanz, R., Bruch, J., Dillon, E., Duardo, D., Dynarski, M., Furgeson, J., Jayanthi, M., Newman-Gonchar, R., Place, K., & Tuttle, C. (2017). Preventing dropout in secondary schools (NCEE 2017-4028). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. https://ies.ed.gov/ncee/wwc/PracticeGuide/2
3 W. K. Kellogg Foundation. (2004). Logic model development guide. https://wkkf.issuelab.org/resource/logic-model-development-guide.html
4Safir, S., & Dugan, J. (2021). Street data: A next-generation model for equity, pedagogy, and school transformation. Corwin Press.
