Today’s fast-paced and often disruptive business climate makes it harder than ever to upskill an existing workforce and ensure organizational readiness to perform.
The challenge of training a vast, multi-generational staff is often compounded by geographic dispersion and the need to demonstrate measurable business impact. It’s no wonder that chief learning officers find it hard to engage diverse skills, abilities and backgrounds all at once; their ranks are broad and multifaceted.
To rise to this challenge, CLOs are turning to adaptive learning, or systems of artificial intelligence that optimize each learner’s experience in real time, enabling him or her to learn as efficiently and effectively as possible.
At their best, adaptive learning platforms slash wasted time, arming CLOs with technology so fine-tuned and intelligent, it’s like having a one-on-one instructor for every learner. To accomplish this goal, adaptive learning is built on three pillars: mastery, personalization and scale.
Mastery-based learning, the idea that learners’ progression through a course is dependent on mastery as opposed to seat time, lies at the core of adaptive learning. The concept is actually intrinsic to corporate life. Our progress up the corporate ladder is based on factors generally unrelated to seat time: achievement, initiative and networking. Progress isn’t about the passage of time; it’s about focus and intensity. Adaptive learning applies this principle to learning: If mastery is what’s important in learning, then why not optimize for it?
Properly optimizing a platform around mastery requires a heightened focus on assessments, the hinges of a learning experience, which reflect whether or not concepts were, in fact, mastered. After all, if learners are progressing based on mastery, what indicates mastery? Assessments generally serve this purpose in any learning experience, but in the case of mastery-based learning, they become more important than ever. Boiled down simply, answering a question correctly or incorrectly (assuming it is a quality question) reveals something about the learner’s state of knowledge. This information about the learner’s state can be used to adapt the course in real time.
Different platforms accomplish this task differently. It can happen simply and straightforwardly by taking large goals, breaking them into smaller objectives and micro-objectives, and then aligning each micro-objective with assessments and learning resources (or any sort of learning content) in the platform. Structuring the content this way gives the learning experience a mastery-based framework. Learner interaction with the content can then generate a data layer that powers not only adaptive learning but also agile authoring and tailored instruction. As learners begin taking the course, the platform gives every stakeholder – learners, instructors and authors – granular insight into a range of performance metrics.
Adaptive learning takes this concept of mastery one step further and personalizes the pursuit of mastery, so that each learner progresses not only according to his or her proficiency level but also based on confidence and engagement, which are also significant factors in a learning experience.
To fully personalize content, adaptive learning incorporates algorithms. Four theories work together to give an adaptive learning platforms its reflexes, the way an instructor might pivot strategies from learner to learner:
The first, metacognitive theory, holds that people learn best when they know what they don’t know. As learners move through content, the platform captures data on accuracy, confidence and time. Armed with these data, the platform can instantly adjust content to not only help each learner increase accuracy but also to improve awareness of accuracy, so that the learner walks away knowing what he or she knows and doesn’t know.
The second principle, the theory of deliberate practice, suggests that understanding weaknesses helps refine and focus practice. To address this principle, an adaptive learning platform continually tailors content based on the individual learner’s weaknesses, saving time and focusing energy for maximum efficiency.
At the same time, this theory should be tempered by the theory of fun for game design, which holds that learners are most engaged when they are challenged but not too challenged. If too many questions are consecutively answered incorrectly, for instance, the platform will deliver a question that provides a quick win to build confidence and increase engagement.
Finally, the Ebbinghaus forgetting curve holds that to truly learn something, learners must commit it to long-term memory, and that the best time to do so is just as learners are about to forget it. Incorporating this theory, the platform can use data to predict when a learner is likely to lose a concept from short-term memory. It can then automatically reintroduce this content just before it slips away, solidifying it in the learner’s mind as long-term memory.
It’s called adaptive learning not just because it adapts seamlessly to a variety of learners, but also because it’s flexible enough to transform almost any content – technical, qualitative or quantitative – into an adaptive course. In the corporate space, this pillar is absolutely crucial. Whether it’s technical skills, accounting or public speaking, a successful adaptive learning platform must be able to take any content and re-route it into a modularized course that’s responsive to each learner’s specific needs and capabilities.
That’s the future of corporate learning: flexible, scalable platforms refined by millions of learners and thousands of authors, capable of transforming corporate learning at the highest levels, across even the most dispersed and diverse workforce.