How data can solve problems to support the growth of hybrid learning

Data can help administrators forecast teacher turnover and design hybrid learning programs.
Lewis Perry
Lewis Perry
Lewis Perry is field sales director at Qlik. He focuses on providing scalable data analytics and integration solutions for K-12, higher education institutions, and state and local government agencies.

It’s a harsh reality—teachers are quitting their field at a rapid rate, and the pandemic isn’t all that’s to blame.

By nature, teaching is all about relationships—understanding students’ needs, fostering their passions, and figuring out what makes them tick. And over the last two years, these relationships have been not only tested but in the worst circumstances, they have been completely lost.

Of course, we’re aware that hybrid learning took its toll on students, but it also negatively affected teachers. Between health concerns, evolving curriculums and learning environments and demanding regulations, teachers’ levels of stress and burnout have increased dramatically throughout these unusual pandemic times, raising concerns about a potential increase in teacher turnover and future teacher shortages.

Typically, 8% of teachers leave the profession every year, but the pandemic has exacerbated these numbers dramatically. In a recent survey, 54% of teachers said they are “somewhat” or “very likely” to leave teaching, compared to just 34% of teachers who said they would have answered that question with “somewhat” or “very likely” if they’d been asked in the fall of 2019.

Using data to prepare for teacher shortages

While some teacher attrition is normal, as it is in any profession, these extreme numbers are having a large impact on schools across the country, and administrators are looking for ways to help counteract this challenge. Existing data, and the predictive analytics that can help make sense of it all, may be a solution.

First, data can help administrators predict specific roles or specialties that individuals will leave. By looking at teacher departures from previous years, you can determine where it’s likely that your school or county will experience future attrition. As a result, you can coordinate efforts to send more resources to these disciplines, whether it’s funds, substitute teachers, or additional training and recertifications for existing personnel. It’s common for certain specialties, like special education, math and science, to have higher turnover rates. By utilizing retention data from years past, administrators can make better-informed decisions about how to prepare for these departures—or to help course-correct issues before they cause educators to leave.

More from DA: Is your school district located in one of America’s most-educated cities?

As a result, data can also help administrators anticipate problem areas—and solve them—before they cause further teacher turnover. If a school or district conducts exit interviews with departing teachers, it likely has insights into why they decided to leave. As a result, the team can use predictive analytics to help sift through this information, identify themes in these exit interviews, and then help address some of these common concerns before they cause future departures. Say that in the last year, 45 percent of your teachers that left cited a lack of resources as a major reason for leaving. Having this information, an administrator can work with the district leaders to better provide schools’ educators with the resources they need to be successful in years to come.

Accommodating larger, hybrid classrooms

While data can support administrators as they work to minimize the number of teachers who leave as a result of the pandemic’s impact on the profession, the problems resulting from recent teacher departures are already impacting students. With fewer educators available, schools will be forced to accommodate larger classrooms on a long-term basis, likely causing a permanent move to a hybrid model.

If provided access to data and the tools to best analyze it, schools can use this information to apply for additional government funding to accommodate long-term hybrid learning. Administrators can use existing information and models from the last two years of education to identify where the school needs more resources and funding. Once they receive these resources, they can leverage existing data to be sure they’re allocating them to the areas with the most need.

Data from the last two years can also help Identify students who are already experiencing—or are at risk of experiencing—educational disparities through hybrid learning. Research shows that specific demographics and areas of the country were more severely impacted by hybrid and at-home learning than others. Using this information, school districts and educators can create education plans and digital curriculums that help address these learning disparities experienced by historically underserved student groups, including students of color, disabled students, or those from low-income backgrounds.

By using existing data and implementing predictive analytics to best understand and act upon these insights, district administrators and educators can prepare for today’s education realities, and ensure the classrooms are working for students and teachers.

Most Popular