It took the most disruptive pandemic in the past 100 years to highlight the inflexibility between traditional school systems and blended learning environments.

When students returned to school during the mandated lockdowns for Covid-19, reports showed that nearly 60 percent of districts were providing some form of remote curriculum, instruction, and progress monitoring.

Unfortunately, the coronavirus suddenly forced most teachers to engage with their students without their traditional tools for observations and evaluations. Many reports did a great job highlighting the results, including an article by the New York Times titled “As School Moves Online, Many Students Stay Logged Out.” 

The concept of blended learning environments isn’t flawed.

As the world becomes more internet-based, blended learning environments are naturally considered an innovative approach for school districts that want to streamline the way students naturally consume technology into their learning process. They are also an alternative structure for students who aren’t stimulated by traditional settings of synchronous learning.

The bad news is that the results seen during the pandemic are all too common in blended learning environments. Considering the fact that low student engagement directly correlates with substandard educational outcomes, there hasn’t been much detailed analysis about engagement in blended learning environments, excluding the accounts where economic factors disallow low-income students from the advantages of their peers who have higher technological equity. 

For a year, I served as CEO of one of the first virtual schools in Illinois.

In that role, I ultimately learned that disjointed data creates a huge blind spot in these environments.

Imperfect data in the distant learning environment constantly pushed me to toggle between being “Cheryl Pruitt, the executive” and “Cheryl Pruitt, the non-empirical empiricist.”

Yet, the struggle with blended environments isn’t directly generated by the platforms that help transfer fundamental principles from synchronous learning to asynchronous learning. Instead, the pandemic underlined what school officials and parents in blended learning environments have known for a long time: the methods used to track student engagement are largely undefined, beyond simple attendance records.

Traditional measures of student engagement predominantly rely on student self-report instruments, which are subsequently supplemented with teacher evaluations. Those measures are enabled by a setting where teachers regularly observe their students from an empirical vantage, which doesn’t translate to a distance learning setting.

Even if you use “attendance” to try and force insights within a blended learning environment, you will likely run into the fact that there is not a universally standard definition for it. For example, a survey released in July by Insight Policy Research, titled “Promising Practices Brief: Improving Student Engagement and Attendance During COVID-19 School Closures,” some districts tracked attendance through submission of assignments, while others distributed periodic “checkpoints.”

Because these methods of evaluation are undefined, they stall collaborative efforts between teachers, parents, students, and administrators. Even more, the lack of definition creates such a systemic disconnect that some administrators don’t recognize just how ill-equipped they are to make data-driven decisions, which I can attest, going back to my time as “Cheryl Pruitt, the executive” vs “Cheryl Pruitt, the non-empirical empiricist.” 

This is why results from a recently published “Biosensors Show Promise as a Measure of Student Engagement in a Large Introductory Biology Course” are incredibly promising.

In the study, researchers used unbiased biosensors to collect biophysical data about engagement.

The results showed statistically relevant correlations between the biosensors, the students’ own self-reflection engagement forms, in addition to evaluations from instructors. Furthermore, they proved that scientific analysis of student engagement may exist to bolster blended learning environments.

Without question, distributing biosensors to children sounds like a major political fight, especially without transparent and well-defined privacy rights. Still, studies like this encourage school officials to continue to think outside the conventional realm of known methods to find ways to inform teachers with statistically-relevant feedback about student engagement.

Without better analytical tools for teachers and administrators, the promise of technological advancements in the education system won’t keep pace with the same societal advancements that result from Moore’s law.

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