Adaptive Learning 3.0: Beyond Branching & Algorithms
“How does your software adapt?”
It’s a question we get asked all the time, and it implies the understanding that all adaptive learning systems are not created equal.
The Adaptive Learning Spectrum
If you have prior experience with adaptive learning systems, you know that there’s a wide spectrum of what “adaptive” can mean. “Adaptive” learning can range from:
- Simplistic Branching: Make adaptations based on decision trees (branching) and pre-diagnostics
- Limited Algorithms: Make limited adaptations based on predetermined algorithms
- Advanced AI: Make complex, real-time adaptations based on cutting-edge AI and machine learning
Historically, most adaptive learning systems have fallen into the first two categories. This is because, while advancements in machine learning and the application of deep neural networks are becoming mainstream today, just a few years ago this technology was on the fringe. Many learning companies understood the potential of AI in learning, but didn’t have the expertise to build out truly adaptive systems.
How Different “Adaptive” Categories Compare
While solutions on the lower end of the adaptive spectrum still offer more personalization than a traditional one-size-fits-all approach, they don’t really deliver on the promise of adaptive learning. The table below demonstrates the differences in capability:
New Adaptive Providers: Branching out from Branching, Evolving from Advanced Algorithms
“Thanks to advances in artificial intelligence (AI) and machine learning, a slow but steady transformation is coming to education…” – PCMag
Recently, tech giants like Google and Apple have open sourced their machine learning frameworks, making it easier for companies to develop intelligent solutions. In the learning space, some providers are taking advantage of this innovation. These providers are leaving the branching and algorithm-driven versions of adaptive learning behind and integrating newly available AI capabilities into their solutions.
In point of fact, the most advanced learning technologies are moving to the far end of the spectrum and leveraging advanced AI to drive adaptivity. For example, our platform leverages AI and machine learning algorithms to adapt each interaction based on a variety of disparate factors, including: learner answer choices, complexity and weight of questions, time, engagement, optimal patterns observed from other learners in the course and so on. We’re also building out temporal models of student learning and neural networks so our technology can better understand how users learn and, in turn, provide more informed processes, recommendations and predictions. And as a result of the work we’ve done in AI, Fulcrum has just been shortlisted for on Education Technology Insights Magazine’s “Top 10 Artificial Intelligence Solution Providers 2018”.
If you’re interested or curious about modernizing your training or education program, we can help. Check out our overview video to get a feel for how we approach adaptive learning and then let’s connect.