Paul Hill urges reporters and analysts to make apples-to-apples comparisons when analyzing research on school outcomes.
Following the Money in Personalized Learning
Reorganizing time, talent, technology, and physical space to support personalized learning takes money, planning, and time. Dozens of philanthropies, new support organizations, and policy groups are dedicated to helping schools implement this model. To date, philanthropic dollars have footed the bill for most start-up costs in many personalized learning schools.
However, if personalized learning models prove promising enough for more widespread adoption, we’ll need to know how they can be developed and sustained at scale.
Our center has taken the first systematic look at what implementing personalized learning schools costs, how school leaders are spending their funds, and what it might take to make personalized learning financially sustainable with public dollars. We studied 16 charter elementary and secondary schools with a wide range of personalized learning models from across the country (we hoped to include district schools, but the data were not available). All of these schools received financial support through the Bill & Melinda Gates Foundation’s Next Generation Learning Challenges, the earliest and most significant philanthropic investment in personalized learning to date. (The Gates Foundation also funded our study.)
While we can’t make overly sweeping generalizations from our research (we looked at a particular set of schools with particular characteristics), this first analysis of personalized learning finances enables educators and policymakers to learn from these early frontrunners and, ideally, to more clearly understand potential fiscal implications.
Here’s some of what we learned. There is no single start-up “cost” to personalized learning. The schools in our sample spent what they had: sometimes a lot, sometimes very little (some as little as $7,400 a student). The implication is that while personalized learning can be costly to implement, it need not be. In most cases, the costs are not much more than typical start-up costs for any new school.
More worrisome to me is the fact that personalized schools routinely underestimated start-up costs in two areas: hefty consulting fees for teacher training and costly facility remodeling and purchasing. These big-ticket items point out the need for better up-front planning and strategy around training teachers to successfully implement personalized learning, as well as the need to carefully think through the costs of creating spaces that are better suited to personalized learning, where students are often required to move in ways that don’t fit the traditional classroom design.
Our study should reassure the many observers who worry that technology will replace teachers in personalized learning models, but should also alarm those who believe that technology is an essential tool to help teachers use their time more effectively. When faced with budget trade-offs, schools typically cut technology to preserve small class sizes and staff positions. But the fact that schools viewed technology as a “luxury good,” and not as an essential instructional tool, is concerning if these decisions were driven by habit and teacher preferences rather than thoughtful analysis of what is best for students and the most productive use of limited resources.
Bottom line: Can personalized learning schools sustain expensive staffing models and technology costs after private funding runs out? That’s an open question. We found that while schools’ proportionate reliance on private philanthropic dollars generally dips as enrollment grows, it’s unclear if some schools will ever be able to run their personalized learning models purely on taxpayer dollars. About a third of schools studied showed significant—and sometimes even increasing—reliance on private funds for ongoing or recurring costs. This reminds me of the early days of charter management organizations: many overestimated how quickly enrollment growth would make them financially self-sustaining and underestimated both costs (start-up and ongoing) and the time that would be needed to develop systems vital to ensuring school quality. Philanthropies would be wise to require those asking for personalized learning start-up funds to demonstrate that their business plans incorporate realistic projections and risk-management plans.
These are still early days for personalized learning. Our study provides what we hope is just the beginning of a strong evidence base for understanding realistic budget and policy planning around personalized learning. The magnitude of dollars—and more importantly, educator and student time—being invested in these schools is significant and likely to grow exponentially. We need to think hard about how to best use scarce public and private dollars so that personalized learning can achieve its promise. Look for more research from us soon about implementing personalized learning at scale.
What do you think of our findings? Share your comments and questions with us, @CRPE_UW, on Twitter, hashtag #PersonalizedLearning.
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