New adaptive learning platforms evaluate your actual knowledge versus what you think you know. It’s a game-changer.
There is a major change in the way eLearning and Training developers are structuring their courses and it helps us get past our illusions about what we think we know and what we actually know. It’s called adaptive learning. The principles that underlie adaptive learning have been around since the 1950s but until there was an easy mechanism to deliver learning to large numbers of people remotely, it was not on the radar of the general eLearning and training world. That has changed drastically.
With translated learning content, the learner is much more likely to be remote from the teaching environment. This article looks at the ramifications of using adaptive learning to improve remote learning experiences.
We all make assumptions about our level of expertise, but are they valid?
Most of us have subjects where that we feel a certain level of expertise. But, oftentimes, that level is not actually realistic. And until now a teacher or trainer would have to rely on asking questions or testing to assess our real level of knowledge. Both of these approaches have only proven marginally useful for learners who are overconfident in their knowledge level. Adaptive learning takes advantage of very basic machine learning processes to assess your actual level of knowledge compared to your perceived level of knowledge. It then adapts the learning you are targeted with to address deficiencies by delivering content that corrects your misunderstanding of a subject.
Courses that ‘learn’ your actual knowledge level
Let’s say you are working through an online course divided into sections by subject. You are taught something and the adaptive learning software follows up with a test of your knowledge and then goes to the next step. It asks you to define your level of knowledge about what you’ve learned. It then pairs up your actual knowledge versus your perceived knowledge and loops back to the areas it defines as deficient, even if you think your competency is higher. The looping serves up new learning content that addresses your mistaken responses. This reinforces your knowledge level and helps you understand where you still have work to do.
The aim is to develop true expertise rather than perceived expertise
Today learning and training often takes place without the live presence of a teacher who can use their experience to understand your actual understanding. Great teachers do this automatically based on extensive experience in common misconceptions. But great teachers are hard to reproduce. And there is an increasing need for constant skills improvement and the ability to access learning experiences when those great teachers aren’t available. Adaptive learning algorithms duplicate the teaching patterns of highly engaged teachers with software that is constantly assessing your actual versus perceived knowledge.
This technology is in its infancy but it promises to revolutionize remote learning
Common testing procedures do not always indicate actual knowledge levels. Humans often misjudge how much they know. This can result in a lot of errors because someone thinks they are correct when they are not. The current state of adaptive learning is the beginning of ways to break this delusional self-assessment model, because it goes beyond simple assessments and analyzes your self-assessment against your actual knowledge level. It then goes a step further and loops back to the problem issues, offering up more ways to see and understand the problem. The process is iterative and as machine learning improves, the courses themselves will get better at this.
elearning developers are increasingly moving to this technology
Most of the major eLearning providers are moving towards these models, either by developing new tools or acquiring more tech-forward learning start-ups. As with AI, the larger the knowledge databases grow through these processes and the better the analytics algorithms get, the faster we get better learning processes with less human intervention. And these processes can scale to millions of learners with far less resources. It’s exciting stuff for education and training, and it’s just getting started.
An entrepreneurial example: “I didn’t know what I didn’t know”
One of the common learning experiences when starting something new is coming to an understanding of your own shortcomings. This can be why entrepreneurship can often be much more difficult than anticipated. Entrepreneurs get excited about ideas that often are unproven theories rather than reality. If they don’t go through a rigorous testing and adaptation process to either validate or invalidate their knowledge, they often get blindsided because of the assumption of knowledge. This is also known as learning the hard way. Technologies like adaptive learning offer the potential to get real feedback from the learning process that clearly identifies our shortcomings and then tries to address them. I suspect its impact is going to be significant as the technologies are refined and tested.
As we move to new learning technologies and learning that is delivered digitally rather than in person by educational professionals, the more important it becomes to duplicate the processes of the most effective teachers (those whose students move on to become experts, for example). Adaptive learning, though in its infancy, offers the promise of higher quality and more realistic learning experiences, rather than the pure information dumps that earlier online courses often represented.