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Aurora Institute

What Life before EdTech Can Teach Us about Personalized Learning

CompetencyWorks Blog

Author(s): Julia Freeland

Issue(s): Issues in Practice, Lead Change and Innovation, Rethink Instruction

TypewriterThis post originally appeared at the Christensen Institute on July 21, 2016.

In many circles, edtech and the future of learning have become synonymous. This is unsurprising given the enormous uptick in online courses and technology tools in K–12 schools nationwide, not to mention the promise that technology holds to dismantle barriers to access and experience that have plagued the education system for years.

Yet, with excitement over new gadgets and possibilities, schools and edtech entrepreneurs alike often miss a key step: defining what the ideal student experience should look like absent technology. Before building, trying, or buying new technology tools, we should start by asking, “In an analog world, how would we personalize learning to drive student outcomes?”

This question can refocus edtech enthusiasts on the right unit of innovation: instruction. All too often, when we talk about edtech innovations, technology takes the lead and instructional models are relegated to playing a supporting role. By first taking the time to imagine an ideal tech-free instructional model, schools can avoid the temptation to merely digitize their traditional systems or cram hardware into classrooms ill-equipped to take advantage of what technology can offer. Instead, by establishing an ideal vision of learning in a tech-free world, schools and edtech companies stand to more effortlessly deploy technology in a manner that predictably drives outcomes in the long run.

My colleague Thomas Arnett’s new case study, “Connecting ed & tech: Partnering to drive student outcomes,” highlights an example of a school, and one particularly innovative teacher, that took this order of operations to heart. Starting in 2008, Michael Fauteux, a veteran math teacher at Leadership Public Schools (LPS), created Academic Numeracy, a companion math course to Algebra 1 for all 9th graders who were below grade level in math. The course was designed in line with two textbooks and a supplemental online software program that Fauteux’s colleague, Todd McPeak, had developed. After showing dramatic results in students’ math scores, LPS expanded Academic Numeracy to all three schools in the San Francisco Bay Area network the following year.

Yet, although Academic Numeracy was driving students’ test scores, it didn’t appear to be helping the most advanced or lowest performing students stay engaged in class. Fauteux tackled this challenge by adding additional structures and opportunities to tailor learning to his students’ individual needs—he held small-group instruction before and after school and manually gathered data to monitor individual student progress. He even posted a “data wall” in class where students could see and celebrate their progress.

But still not satisfied, Fauteux wanted to push the model even further so that students could have greater control over their learning. To that end, he built a low-tech tool in Google Sheets that he called Learning Lists, wherein students could see their learning data, make decisions, and take more responsibility for their own learning. By shifting some of the instruction to online resources through Learning Lists, Fauteux could devote more time to teaching students skills like note taking, goal setting, and reflection.

It was not until Fauteux had honed and scaled this relatively low-tech model across the LPS network that the school delved into a partnership with Gooru, an edtech company that was building a platform to support personalized learning. Together, LPS and Gooru created a powerful platform that streamlined many of the processes that Fateaux and his colleagues had come up with over the years. Crucially, by that point in time, LPS’s challenge was not coming up with an instructional model. Instead, the challenge was building a tool that would make a successful instructional model run more smoothly and efficiently and make adopting the model more seamless for new teachers. And it was at that very point that Gooru’s engineers could successfully channel the power of technology to support truly student outcomes.

LPS and Gooru’s experience illustrates why instructional models should inform technology, not the other way around. Technology has proven its incredible potential to unlock models of learning that were nearly impossible at scale in previous decades. But we mustn’t lose sight of the fact that Fateaux successfully restructured his class from a teacher-led instructional model to a student-directed one using just spreadsheets and elbow grease. The new technology powering LPS’s personalized learning model is the result of numerous years of instructional innovation that predated the technological innovation of their new platform. By focusing our innovation energy on instruction first and technology second, investments in edtech are more likely to pay off in the long run.

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Julia researches innovative policies and practices in K-12 education, with a focus on competency based education policies, blended learning models, and initiatives to increase students’ social capital.