Design Parameters for Personalized Learning

As I meet with education leaders across the country, I am often asked questions about the best way to roll-out personalized learning within a district. I always struggle to answer this question since it entirely depends upon the unique needs and circumstances within each community. In some ways, it feels as if someone is asking me, “What’s the very best car I can buy?” Each vehicle option includes trade-offs, and the optimal vehicle will depend on the specific needs of the driver.

For example:  

• A truck would provide the most towing capacity, but parking in an urban setting would be difficult.

• A compact car would give the rider the best commuting option, but cargo room would be limited.

• A minivan will provide space for passengers, but...then you’re driving a minivan. :-)

Establishing the initial rollout for personalized learning similarly involves trade-offs and options. The best deployment will take into account the unique needs and circumstances of the specific district, as the examples below demonstrate.

• Yuma Elementary School District 1 launched its implementation across all 17 schools at the same time to create systemic alignment throughout the schools. As well, this model developed a strong sense of equity as teachers and students engaged in the process together.

• Loudoun County Public Schools created an application process for schools to engage in the personalized learning process. If not selected in the initial implementation, schools can participate in subsequent waves.

• The Enlarged City School District of Middletown allowed all teachers to opt into a specific wave of training over three school-years when they felt prepared. In this model, teachers could jump right into the process, or “wait and see” to learn from others.

This infographic, Design Parameters for Personalized Learning, is intended to help districts as they embark on a journey toward personalized learning by outlining the various decisions they can make as parameters are designed. Identifying initial parameters certainly involves trade-offs; creating opportunities for teachers to choose the model, timing, or focus area will lead to a greater variety across a system – and, therefore, a lack of alignment. Alternatively, aligning on the models, timing, or focus area will limit the autonomy of teachers.

Just as selecting a vehicle can be tricky as one considers their needs, starting the path to personalized learning can be difficult. We hope this guide will assist you in your journey!