Learning labels are a standard representation of expectations for a task or experience, a way to define learning in skills and skill quantifiers. The goal is to get practitioners and/or publishers to create collections of learning labels. Once the labels are in place, use them to achieve higher level goals - like those of a project, lesson plan, course, or job.

With a collection of labels, a practitioner creates a series by connecting them together based on performance, tasks them to learners, projects, or syllabus, and then grades the learners (or lets the system do the grading through a quiz interface). See this video on how this works. Using a separate app / interface a learner then navigates through the series to reach the learning objectives.

 

  • This works with the structure of a traditional or competency based course. This could be a way to keep higher performing learners engaged, while providing added resources for the challenged learners.
  • This works with an onboarding process. More and more companies are hiring new employees for a ‘trial period’. These series work well in identifying employees skills and making sure they match the job requirements. There are suggested ways to move forward (resources to reskill or upskill) and also end points (where a worker needs to find another job). 
  • This works with micro credentialing (if the interpretation is on a task level, rather than a shorter interval). A series is growable, so could provide a constant feed of tasks.

To work properly, there needs to be enough learning labels in the system. A learning label is universal, so once a label is created, the definition does not change. So through time, a practitioner might create a large collection of learning labels. Moreover, (ideally) the learning labels get shared among practitioners and publishers of learning resources.

So creating a series based on percentiles (10 scenarios) might not seem realistic right away, though a practitioner could cultivate the series through a couple of years - increasing the number of possible scenarios and tasks in the series.

Finally, much of the series could be suggested with machine learning and AI once there are a sufficient number of learning labels. Clearly, this is one way to advance the system.