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or the majority of human history, to learn a trade, an apprentice worked side-by-side with an employer, learning directly from him (or her) and then practicing the craft until they perfected it. Employers didn’t just give the apprentice a multiplechoice test to evaluate their readiness. Rather, they watched how an apprentice behaved and performed, and then provided feedback — ensuring the apprentice learned the necessary skills and could confidently apply them.
WHY ONE-TO-ONE LEARNING? The days of the traditional apprenticeship are long gone. But, as study after study has shown, this level of personalization and one-to-one interaction is hard to beat when it comes to its efficacy and efficiency. Additionally, studies also show that one-to-one learning promotes greater learning, increased motivation,
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and also enhances persistence, retention and degree attainment. Why is it so effective? One reason is that in the one-to-one scenario, the coach isn’t just paying attention to what a learner knows. They are also observing and adjusting based on learner behavior. For example, a coach is gaining actionable insights by evaluating what a learner does when they haven’t mastered a skill: Do they go back and source the skill? Do they attempt to problem solve? Do they lose confidence? Based on a learner’s behavior, the coach can offer individualized remediation and practical recommendations for improvement. They can also optimize the level of difficulty of the learning, making for a much more effective and efficient learning experience. Historically, one-to-one learning hasn’t been widely adopted because it’s not scalable and often not logistically possible.
However, we have entered a new era of possibilities. Through a combination of learning science, artificial intelligence and machine learning, UI/UX, and data analytics, the most advanced digital learning platforms are leveraging new insights — derived from behavior and knowledge mapping — to emulate the personalized, one-to-one learning experience at scale. And in doing so, they are tapping into the other 50 percent of the learning equation.
WHAT IS BEHAVIOR AND KNOWLEDGE MAPPING? Behavior and knowledge mapping leverages machine learning algorithms to monitor learner behavior (the choices that they make within a learning platform), along with learning performance, to dynamically adjust content, hints, feedback, recommendations and “nudges.” Through behavior and knowledge
