Wayne Skipper // September 26, 2018

Can Education Keep Up with Technology?

Keeping up

This post originally appeared in EDUCAUSE Review in September 2018.

Trends such as automation and artificial intelligence (AI) are not just concerns for the future. With machines expected to handle over 50% of workplace tasks by 2025, it is imperative that we begin to take seriously the rapidly accelerating pace of technological change. Even jobs that have long been considered to be the exclusive purview of elite graduates—doctor, lawyer, hedge fund manager, even CEO—are now being impacted.

A recent report from Burning Glass and the Strada Education Network finds that 43 percent of recent college graduates are underemployed in their first job out of college. With job-hopping becoming the primary avenue by which workers can obtain a significant pay raise, it should be no surprise that data from the Bureau of Labor Statistics highlight another accelerating trend: a decline in the average length of employment.

Despite record-low unemployment, employers report increased difficulty in hiring skilled workers. Call it a skills gap or not, it seems clear that some sort of gap exists in our ability to match workers to jobs at the pace needed by employers. This is another trend that seems likely to accelerate as employers seek to increase already record high levels of worker productivity.

So how can we prepare students for a world in which the skills they need to succeed in the workforce change so rapidly while best matching them to the jobs available to them today? The solution lies in the quantification of learning itself.

Machine-Readable Learning

Historically, student learning achievements have been represented in monolithic, analog formats such as degrees, which derive their value to a large degree from the reputation of the issuing institution. However, a degree communicates to an employer neither the specific skills a student has learned nor what level of mastery a student has demonstrated. Instead, it primarily communicates the amount of time the student has spent earning the credential.

Competency-based learning is an alternative to time-based units of measure that offers a much more granular understanding of student learning outcomes, as well as facilitating the design of programs that can be personalized to suit different levels of learning ability. Individual competencies are often arranged into systems called competency frameworks, for example Common Core or the NACE Career Readiness Competencies.

The Competencies & Academic Standards Exchange (CASE) standard from IMS Global gives us the ability to assign a web-based “permanent address” to each competency in a framework, moving us beyond simple keyword matching when discussing skills and into the realm of machine-readable taxonomies.

Common Building Blocks

Open Badges are designed to serve as a common language for describing learning achievements—digital building blocks that allow us to talk about learning achievements from any source in the same way. Everything from annually expiring CPR certificates to industry certifications and even academic credentials can be represented as digital badges. Badges allow us to create digitally verifiable credentials that represent mastery of skills tied directly to a competency’s permanent address in a competency framework.

With the advent of Open Pathways (now part of the Comprehensive Learner Record), we can link Open Badges from any issuer together into stackable learning pathways. This gives us the ability to create portable, machine-readable transcripts of a student’s learning journey that align student learning outcomes with skills attainment. Two primary benefits of this approach are the ability to meaningfully incorporate prior learning assessment and the simplification of transferring academic credit between institutions.

Machine Teaching

A transcript is a look backward on a learner’s journey. A well-structured digital transcript allows us to use machine learning to look forward. By comparing a student’s progress on a learning pathway to the progress of others, we can adaptively guide the student along an optimal path to success, recommending individualized content along the way.

We see steps in this direction in the trend toward personalized learning, but the open ecosystem formed by the combination of CASE, Open Badges, Open Pathways, and the Comprehensive Learner Record gives us the tools we need to implement this at scale—and in a way that ensures transparency and record portability.

A new initiative called EdRec adds a Blockchain-based privacy protection layer to the ecosystem that ensures student learning outcomes data is treated as the property of the student it describes.

Self-Driving Organizations

In the same way that algorithms can manage tasks ordinarily associated with C-level employees, today we see workers around the world being managed by algorithms as part of the gig economy. As we extrapolate this trend deeper into organizations, we see that a surprising array of tasks can be automated and directed by machines.

Soon, we’ll begin to see entire organizations in which the role of workers is no longer to make decisions but instead to set parameters and provide checks and balances. And this may not be all bad for workers. If we carefully design our systems to eliminate unconscious biases, we might find that letting the machines organize our work is beneficial to everyone, perhaps even enabling us to finally address some of the most challenging problems facing the world today.

Enabling Beneficial Change

Given current trends, it seems inevitable that machine-driven teaching and real-time skills matching will become an indispensable part of the future economy. In fact, it might be the only way to generate continued increases in worker productivity. We can no longer expect employers to interpret analog representations of learning achievements. We must instead focus on machine-readable representations backed by an ecosystem of open technology standards.

We have the technology. Our challenge is to ensure that the systems we put in place to address these trends are designed to benefit students and workers, not take advantage of them. We’ve seen clearly the perils of allowing corporations to productize personal data in private data silos. We need to take thoughtful action now to ensure a better future for students. The best way that institutions can help is to require vendors to implement open technology standards as a requirement of doing business.

Together, we can not only embrace the future of work, we can help shape it.