Therefore, learning must be personalized to meet each learner’s needs. But how can this be accomplished in an e-learning environment? Is it possible to recreate at scale, and cost effectively, the experience one would get with a one-on-one tutor? Our answer, developed and refined over the past two decades, is a “biological” approach to adaptive learning.
Traditional e-learning is “one-size-fits-none” — boring, inefficient and ineffective. Area9’s industry-leading biological adaptive engine personalizes your training at the point of delivery – giving every learner a unique experience tailored to his or her needs.
The approach enables a developer to create one set of training content, and let the system do the ‘heavy lifting’ of adapting that content to the needs of each learner.
What matters most in providing the needed support for vastly different preferences and personalized paths is understanding how each individual progresses through his or her learning, while keeping track of what has been learned and the content that is most closely related. That’s the role of the biological model.
Fully Adaptive (Personalized)
Every learner is unique, with a specific set of skills, experiences, background, attitudes and motivations. To be effective, learning must adapt to these differences. Our biological model adapts to the unique needs of every learner, optimizing the experience and helping the learner achieve mastery in the most efficient and effective way.
Formative Assessment-based Approach to Teaching
Traditional e-learning teaches content and then asks questions to test what, if anything, has been learned (typically testing short-term memorization of a small subset of content). Our adaptive approach uses formative assessment — teaching by asking questions. These questions (or “probes” as we call them) drive the adaptive engine. The questions provide a comprehensive assessment of the learner’s understanding of the material, and focus the adaptive engine exactly where the learner needs most help.
Answering questions is one aspect of the adaptive approach, but equally important is the learner’s confidence in his or her answer. Every probe checks for confidence – did the learner think he or she knew the answer – and compares that to how the person actually performed. We use this in many ways in the adaptive engine, but one major outcome is the measurement of “unconscious incompetence” – things learners thought they knew, but didn’t.
Unconscious incompetence is a hidden cost in business – typically 20% of what employees think they know is actually incorrect. This leads to costly mistakes. Our confidence-based approach discovers and corrects unconscious incompetence – saving millions of dollars in workplace errors.
Recharge: Personalized Refresher to Ensure Maintenance of Proficiency
The human brain requires multiple exposures to stimuli to build long-term memories. Our adaptive learning platform addresses these cognitive gaps to help exploit them. Our platform helps repeat exposure to each learner’s most difficult learning items to “recharge” memories and drive understanding.
Adaptive learning technology
Data from Area9 Learning’s corporate clients shows that employees can be between 20-40% “unconsciously incompetent” in critical competencies that they are required to master in order to perform their jobs. Employees who are “unconsciously incompetent” about product features (or about any aspect of work) are a huge liability and an obstacle to unleashing the full potential of your business.
Adaptive learning technologies can highlight where the employees have knowledge gaps and remediate these areas. That will increase overall competence and reduce liability, improving both company financial performance and the society at large areas.
Offline Capability on iOS & Android
Employees can learn anywhere, anytime with offline capabilities and mobile-friendly options. Courses for web-based desktop, tablet, or mobile interfaces give users access to learning anywhere.
Library of all Learning Resources
For those rare circumstances when a learner might want to reference the content directly, bypassing the adaptive engine, we provide access to all resources via a searchable interface.