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The question is often asked, how do we know a course has been successful? How do we know what someone has learned? These are underscored by the deeper question of whether we can trust in the education of our mechanics, doctors, engineers and pilots.

The problem is intractable because there is not clear agreement on what counts as success. The different outcomes from learning events can be tracked and measured in any number of ways. And all the while, there is the danger of bad actors - of those who cheat on tests, fake certificates, or misrepresent their qualifications.

In recent years we have seen renewed focus the idea of competencies and competency definitions. The American Advanced Distributed Learning initiative has launched the Competencies and Skills Systems program, for example, part of their wider Total Learning Architecture.

There are numerous competency definition standards, everything from Australia’s National Competency Standards to the NIH’s Nursing Competency standard. Activity tracking has been formalized by xAPI and records are stored in Learning Record Stores (LRS). These systems are gradually migrating toward a decentralized linked data model, as exemplified by the suggestion to develop a blockchain network for badges and certifications.

As a result, we need to think of the content of assessments more broadly. The traditional educational model is based on tests and assignments, grades, degrees and professional certifications. But with activity data we can begin tracking things like which resources a person read, who they spoke to, and what questions they asked.

We can also gather data outside the school or program, looking at actual results and feedback from the workplace. In the world of centralized platforms, such data collection would be risky and intrusive, but in a distributed data network where people manage their own data, greater opportunities are afforded.

While no doubt people will continue to collect badges, degrees and certificates, these will play a much smaller role in how we comprehend how and whether a person has learned. The same data set may be analyzed in any number of different ways and can be used by learners as input to evaluation services that use zero-knowledge methods to calculate an individuals status against any number of defined (or implicit) employment or position requirements.

The skills of a traditional learner - passing the test and meeting the expectations of a teacher - will be replaced with a more concentrated focus on developing a unique set of skills and capacities (and a body of work to support that).

It might be said that the certificate of the future will be a job offer. Already software is being developed to map directly from a person’s online profile to job and work opportunities (this is how one of our projects, MicroMissions, works in the Government of Canada). These profiles today are unreliable and superficial, but with trustworthy data from distributed networks we will be able to much more accurately determine the skills - and potential - of every individual. And I think we’ll be surprised by what we see.

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