Where we are
Just as there are many definitions of personalized learning, there are also many different pedagogical approaches to it. If you go to any big education conference, you see innovators from the tech side are trying everything under the sun. To discover which of these approaches are the most effective, we need to study them individually. Personalized learning as a whole is too broad and varied to evaluate effectively, so we need to study the specific approaches to determine which are the most effective and with which students.
According to observational studies by Renaissance, one major obstacle to personalized learning is planning. The average teacher spends eight to 10 hours a week planning. Because they’re supervising children and teaching classes, those hours must occur before school, after school, on nights, or on the weekends. If teachers are working that hard to prepare an instructional sequence that is, in many cases, shooting to the middle, unless we can alleviate that pressure point, there’s very little hope that educators are going to routinely personalize learning. With this in mind, we firmly believe that the next iteration of technology to support personalized learning will revolve around planning.
Where we need to go
No one company has enough resources to meet all of the varied planning needs of all teachers, so I believe our best chance at making personalized learning a reality is creating an open ecosystem of resources, helping teachers find what they need, then connecting these resources together through assessments.
When a student takes a math or early literacy assessment, it should do more than just show the teacher what skills that student needs to work on. It should go a step further and identify resources the teacher can use to teach that skill and then re-assess it.
We also need to create a round-trip journey of data where results from each activity, regardless of the content provider, flow back to a central place. In many schools, valuable information gets lost because it’s not in an electronic format and it’s not catalogued against learning progressions or skills. If all that information was connected through assessment and a clear progression of skills, educators could gather real insights about individual students in a dynamic way, which would take them a big step closer to truly personalizing learning.
How are we defining success?
At the end of the day, we should also consider how we’re defining success for personalized learning. Does it have to solely mean that a student goes on to a higher level of performance? If we involve them in the process and we activate their student agency by using different approaches with them, maybe we won’t send the overall proficiency rate or the NAEP score numbers through the roof—but what if we can save even a portion of the kids that drop out?
Some metrics estimate the impact of a single dropout on a community can be in excess of $100,000 over the course of that person’s life. High school dropouts require more support. They’re going to make less; they’re more likely to have social issues and potentially to be incarcerated. So, if personalized learning can cut our national high-school dropout rate in half, the economic and academic impacts would be huge.
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