What questions do you have about substitute teaching in your district? Curious about where subs choose to work? When demand for subs peaks? How frequently subs work? Chances are many of those questions can be answered by digging into the data you are already have available through your substitute management system.
In the last few months we have been surprised by just how much you can learn through analyzing substitute data and how infrequently districts are digging into this rich data source.
Here are a just three critical insights we have uncovered, we hope they pique your curiosity and get you thinking about what you might learn from your own data:
1. No Monday/Friday Peak: Every district we have sat down with shares the assumption that it is harder to cover teacher absence on Mondays and Fridays, mostly because demand is higher, with more teachers absent. It makes intuitive sense, because who doesn’t like a three day weekend? While it is possible that HR and school offices are working harder on these days, the data we’ve gathered at Substantial suggest that both demand and coverage is fairly consistent through the week, with no big spikes on Mondays or Fridays. If we see slightly lower coverage on a Friday, it’s usually because of supply—not demand—as fewer subs are choosing to work.
2. Different Districts Have Different Pools: In one district, 74% of substitutes are over the age of 50 and most work around one day a week. This seems to back up the anecdotal evidence that retired teachers dominate their pool. In another district, the vast majority of subs are women in their 30’s and 40’s who work at a specific school site. This suggests a pool made largely of parents who have a connection to a specific school. Understanding your substitute pool is critical because the needs and motivations will be different. By knowing your pool, you can develop meaningful support and tackle supply challenges more effectively.
3. Coverage Varies Dramatically: Within a single district substitute coverage rates can vary dramatically among school sites. If districts are looking at data, it’s often in the aggregate, and these nuances can be obscured. For example, in a district with an average of 80% teacher absences covered, one school may be at 99%, and another is languishing at 54%. What’s intriguing is that it isn’t always the schools you expect. We’re learning a lot by focusing on the schools that surprise us and challenge our assumptions about where subs want to go.
From our early analysis, one thing is very clear—understanding the root cause of substitute system challenges is critical to ensure you’re designing strategies that will work for your district and your subs.
What will you learn from your data?