True adaptive learning has to measure and deliver knowledge, behaviors and outcomes
Is your current learning system really adaptive? If it isn’t building an ongoing, multidimensional picture of what your employees know, do and deliver, then the answer is no.
Consider this: A learning system that continually crunches the kind of data that matters to your business. A system that uses technology to deliver customized learning to each of your people based on the results of that data. Learning that automatically evolves and adapts to the unique and changing needs of each of your employees. A learning program that doesn’t require justification because it is so intrinsically tied to your business outcomes. Sound like an L&D utopia?
It’s not. Adaptive learning done right offers all of that.
Many learning platforms have responded to the growing interest in adaptive learning by adding it as a buzzword to their sales pitches alongside gamification and mobile learning. But adaptive learning deserves more than just lip service.
You probably know what adaptive learning is. Learning that leverages technology to adapt to the learning needs of our employees. In its basic form, adaptive learning takes data about existing employee knowledge and then delivers the knowledge that will meet the learning needs of that specific employee.
But don’t be satisfied with either lip service or a basic approach.
True adaptive learning requires ongoing analysis of three pieces of multidimensional data: knowledge, behavior and outcomes. It isn’t enough to measure this data in separate, neat streams. And you can’t just measure it once. Your algorithm has to consistently and constantly measure all three of these data streams while analyzing the combined results to deliver learning. This is critical stuff. Imagine being able to make a visible connection between L&D and profit. True adaptive learning makes this possible.
What does your employee know today? What will s/he know a week from today? True adaptive learning gathers data about employee knowledge continually, but it goes even deeper than that. It also tracks changes in employee knowledge including those inevitable times when employees lose previously mastered knowledge.
Adaptive learning should also measure confidence. It doesn’t matter how well you know something if you don’t feel like you know it. This is a seldom measured but critical component of knowledge. Lack of confidence in knowledge is just as dangerous as overconfidence without knowledge and both should be caught and addressed in a truly adaptive learning program.
We can’t make the assumption, as old learning systems do, that learning is a constantly forward moving proposition. Knowledge definitely propels us forward. Knowledge levels also occasionally stagnate or falter. It’s inevitable and it has to be an integral part of our learning. True adaptive learning should be able to use data to identify and quickly respond to fill in those knowledge gaps.
Few learning systems continuously measure knowledge. Sure, they provide test scores and participation rates. But what does that tell you? Sadly, fewer still take analytics beyond knowledge testing.
Remember the old maxim, “Don’t tell me what you know, tell me what you’ve done”? Knowledge is only one piece of the puzzle. You don’t just care about what your employees know, you care that they use this knowledge. What good is knowledge if it isn’t informing behavior? True adaptive learning considers data regarding employee behavior and then weaves it together with data regarding employee knowledge to get an accurate picture of how knowledge is affecting behavior.
Sure, your people passed the course on ladder safety but are they actually using a ladder safely? And maybe they understand the importance of regulatory requirements but are they actually considering them in a risk assessment? Algorithms can analyze behavior data that allows you to identify the overall effectiveness of knowledge delivery and to identify potential gaps. True adaptive learning allows you to get ahead of these potential gaps. If your L&D program isn’t delivering the behaviors you want to see—whether you’re looking to increase sales, reduce safety incidents or ensure compliance—real adaptive learning will tell you why.
Behavior data can be gathered via observation from senior staff or, as some companies are doing, by specifically hired observers. The more data points the better. These are then fed into an algorithm and used to automatically drive learning. With a truly adaptive system, these data points will be tied to specific knowledge and the algorithm will assess and adjust learning based on both the individual’s behavior and knowledge. How is that for going beyond simple test scores?
The final piece of data that adaptive learning must consider is the most important from a whole company point of view which is why we’ve saved it for last. But outcomes should be your first consideration and should drive all of your L&D.
What results do you want to see? What are your company’s objectives?
It seems pretty obvious, but surprisingly this is where most learning systems fall apart. They don’t make the connection between the outcomes you want, the behavior required to achieve those outcomes and the knowledge your people need to have to feed those behaviors.
True adaptive learning makes these connections.
Is the new approach to customer service actually delivering sales? Are safety incidents down? It’s the big picture. It’s what matters to the rest of your company and L&D needs to deliver on outcomes in order to stay relevant. How can you do that? You need a system with an algorithm that continually takes data regarding outcomes and analyzes it alongside data on knowledge and behavior.
And you need a system that responds quickly and automatically to deliver learning based on the results of this data in real time. This is true adaptive learning.
Knowledge, behaviors and outcomes. It’s what you want from learning. It is also what you should be getting automatically from your learning system.