Research and the Framework


At LEAP Innovations, we see personalized learning as lever not to reform education, but to transform it. We sought to develop an approach that gives teachers and school leaders the tools to redesign – to reimagine – their teaching and learning practices, to move to a model of education that is centered on the individual learner, and tailored to the unique needs, strengths and interests of each one.

To this end, we worked with practitioners, national thought leaders, and research partners to develop our framework for personalized learning – one that not only defines the core components, but provides actionable classroom strategies for implementation.

At the core of this work is the assumption that educational experiences personalized to individual learners will ultimately produce better outcomes, both in terms of student achievement and the development of non-cognitive skills. However, personalized learning is an emerging field, and there is limited empirical evidence on its relationship to student outcomes.


There is clearly a need for more evidence of personalized learning’s impact on student outcomes. But, before we get there, we must also address a need for new tools to measure the core components of personalized learning.. To that end, we worked with American Institutes for Research to develop the first national teacher and student surveys for personalized learning, as well as a set of standards, to begin measuring the degree of personalization in a classroom. These surveys, rooted in the LEAP Learning Framework, are being taken now by hundreds of educators across the country.

We’ve also developed an observation tool, rooted in the strategies of the framework. Tying this observation data back to responses from the student and teacher surveys, we can paint a clearer picture of personalized learning in action, and then work to relate those practices back to improvements in student achievement.

By measuring teacher practice, student experience, and change over time using our survey and observation data, we’ll be able to identify better indicators of personalization in a classroom – increased student agency, for example, or increased utilization of student data to tailor instruction in real time. Using these indicators, we’ll be able to create better causal connections between personalized learning practices and student outcomes, providing valuable data back to the field and the practitioners themselves on which practices can make the most difference.