With a global pandemic ongoing and “hybrid” learning normalized, K-12 superintendents are under pressure to invest in educational technology (EdTech). The Learning Counsel found that in 2020, spending on EdTech grew by $7.5 billion – a record jump – and is likely to rise by another $2 billion this year. Between the CARES Act and potential funding from infrastructure bills, school districts have an historic opportunity to invest in digital transformation.

The challenge is to build this new EdTech stack on the right foundation. K-12 leaders turn to digital technology not just for learning content and experiences, but insight. Many technologists and educators champion a future where instruction is responsive to each student’s knowledge and abilities. Personalized education may be the key to preventing learning losses and achievement gaps. 

How we build EdTech stacks today will determine whether K-12 districts can provide personalized learning tomorrow. I believe districts need to build these new EdTech stacks on digital identities that represent students holistically, bridge learning systems together, and enable responsive education from the individual to district scale.

Beyond academics

The term “digital identity” refers to more than a set of login credentials. It is a set of data that humanizes students, captures the richness of their K-12 experience, and changes over time. The EdTech stack continuously feeds academic, behavioral, health, demographic, lifestyle, and relational data to this digital identity. There are several important reasons for this approach.

First, tests and grades have limited predictive power. An A-student in elementary school can become a D-student in middle school due to family financial woes, divorce, or health problems. The more holistically a digital identity represents the students, the better educators can correlate learning outcomes to non-academic variables. 

Second, setbacks can happen quickly. Educators don’t want to wait quarters or years to identify a learning loss or gap. They want to catch it and respond in real time. That means educators need the ability to review fresh data from learning platforms as the data is created, not months later when someone has generated a report.

Third, we don’t know all the variables that predict learning outcomes. But if districts collect a wide range of data and analyze it with artificial intelligence (AI) and machine learning (ML) tools, we’re likely to find relationships that manual analyses wouldn’t catch. Digital identities present an opportunity to create new knowledge about students – and a chance to bridge siloed platforms. 

The undivided EdTech stack

In education and numerous other fields, digital technology has an interoperability problem. In essence, each application speaks its own language and can’t share data in a way that other apps would understand. Thus, each EdTech system contains a different “variant” of one student. A digital identity is a construct for aggregating those variants into one representation of the student and their journey.

To illustrate, consider two platforms that offer digital curricula: Connections Academy and Stride Learning Solutions. Both offer courses in numerous K-12 subjects, but they measure progress in different ways. They aren’t designed to exchange information about their shared users. They might give conflicting reports about a student’s progress on math or their proficiency in grammar.

Each platform used by a department or individual teacher is yet another platform and dataset that tells only part of a student’s story. EdTech vendors are not necessarily incentivized to make their systems interoperable. Therefore, an EdTech stack must be integrated at the level of a student’s digital identity. No matter where a student logs in to learn, the stack must recognize the identity and return data back to that one, indivisible profile.

From the individual to district scale

So far, I’ve described an EdTech stack that represents each student with a unique digital identity and aggregates data from each learning system back to that identity. This stack layers context on top of standard academic performance metrics, detects changes in real-time, and correlates outcomes to actionable variables. This is a system that can personalize learning at the individual level and advance it at the district scale.

At the individual level, AI and ML will soon be used to generate dynamic, personalized curricula. A math instructor would continue to plan lessons and in-class exercises and build relationships with students. Meanwhile, homework, quizzes, and tests would be performed on a digital platform that gauges not just students’ trigonometry skills but their other strengths and weaknesses in math. The homework assignments would adapt to each student’s progress, offering assignments designed to remediate learning gaps or push certain students to more advanced problems.  

At the district scale, digital identities enable comparison across cohorts. The data can reveal achievement gaps by race, gender, geography, and other attributes. There is therefore a chance to test interventions and remedial coursework with quick feedback cycles. 

Collectively, data from digital identities can begin to answer big questions about curricula, staff, policies, and learning systems. Which drive positive outcomes? Meanwhile, each student would follow a dynamic curriculum that continuously elevates their potential.

Back to the present

Now that we’ve peered into the likely future of K-12 education, let’s return to the present. There is historic funding and support for digital transformation in K-12 education. Superintendents have a mandate to build EdTech stacks that can grow with our students and serve society for decades to come.

The task before superintendents is to make sound investments. How each district builds its stack could affect students, teachers, and staff in profound ways. If the aim is to build a learning environment that helps students reach their potential, districts need digital identity at the foundation.

About the author

Jim Harold began his career in the enterprise solutions group with Accenture and has since led and grown business in analytics, insights, and Identity Solutions as a software executive at organizations including Teradata, Peoplesoft/Oracle, and Acxiom.  For the past decade, Jim recently has been at the center helping clients leverage Identity solutions to securely connect individuals in a privacy-safe manner.  As an entrepreneur, Jim co-founded the pricing optimization organization Spotlight Solutions which was acquired by Oracle.

Jim has appeared on CNBC and been a frequent speaker at events such as the Forrester Summit, and CRMC.  He is a former member of the Executive Board of Directors for the Sam M. Walton College of Business Center for Retailing Excellence at the University of Arkansas and earned his Bachelors of Science in Mechanical Engineering from the University of Dayton.