- Why and for whom is this tutoring program needed?
- How will this tutoring program increase equity?
- Understand the community strengths, resources and needs through a landscape analysis
- Articulate an equity-based value proposition about unmet student needs identified through the landscape analysis
- Develop a logic model defining inputs, activities, outputs, and outcomes
- Understand program costs and funding sources
- Choose a model design based on:
- Your equity-based value proposition
- Feedback from the community and stakeholders
- Evidence-based research on effective tutoring programs
- The constraints of the context in which the program is operating
(Click on the links below or visit the pages on the lefthand navigation for more information.)
Conducting a Community Landscape Analysis Logic Model Guidance and Template Developing A Value Proposition Tutoring Program Model Dimensions and Planning Tool Beta Tutoring Cost Calculator
Programs should begin by articulating a specific equity-based value proposition informed by an assessment of the community need for tutoring. This foundational clarity will support program leaders to:
- Make purposeful and consistent model design decisions aligned with the program’s value proposition.
- Prevent mission creep and make decisions that serve the needs of the community.
- Scale up quickly and make decisions about trade-offs without the program losing focus.
Program Design should be informed by research.
- While opportunities for further research remain, a solid base of initial evidence can guide program design.
- New programs lack impact data, but being able to highlight that specific model design decisions are based on research will help secure funding sources and build partnerships with school districts or other stakeholders.
Instead of trying to design a perfect program from the start, invest in opportunities for evaluating effectiveness and continuous improvement.
- As one program leader shared, “You could spend three years trying to build the most perfect tutoring program, but our current mindset is: We need to do something now. We need to build in ways to quickly understand what is working (and not working) and quickly course correct.”