Providing training courses in the business learning environment for almost 20 years, learning management systems (LMS) are the conventional way to store and deploy role-based training, but the traditional library of courses falls short in personalizing the learning experience that drives individual employee performance. This one-size-fits-all approach doesn’t provide individuals with the specific knowledge and skills based on their unique needs to perform their roles efficiently and effectively.
In a study conducted by Brandon Hall Group , only 7 percent of organizations felt their learning programs were successful. Furthermore, companies can’t measure learning’s impact on individual and business performance. Leaders want to transform their training to be more optimized and individualized for instruction and learning.
Personalized Learning at Scale Is Here NowToday’s learning ecosystems need the addition of an AI-powered adaptive engine to continuously measure, shape, and guide the learner’s experience based on their prior knowledge, level of engagement, knowledge gaps, preferences, and current performance. Through granular measurement and real-time information, an AI can provide valuable insights to instructors and trainers to help learners achieve their performance goals. Instead of one-size-fits-all, the engine continuously adapts navigational guidance to provide each learner with a personalized journey to success. By leveraging machine learning, the system continuously develops and calibrates its intelligence based on the data it gathers, minimizing the need for configuration.
A key factor in personalizing learning at scale, this futuristic technology is available now. An adaptive learning system works in conjunction with your LMS to enable and accelerate the path to personalization without requiring a wholesale replacement of your existing systems.
In a recent episode of Brandon Hall Group’s Excellence at Work podcast, Manoj Kulkarni, CEO of Realizeit, expounded on the need for personalized learning and what is involved in a successful approach, sharing these key insights:
· Personalization is deeper than a custom playlist. Delivering a personalized interface to select training content is a start, but more important is what’s happening behind the scenes. Personalized training must tailor the learning map, the content, and the resources to be relevant to each learner’s context.
· Focus first on the learner’s needs, not the content. In a one-size-fits-all world, content is primary, and learners adapt to it. With personalized learning, everything shifts to learner-centric. The content and technology adjust to the learner; attempts to focus on content first always result in a veiled approach to personalization that remains one-size-fits-all.
· Scale to the entire organization. It is possible to provide personalized learning experiences at a small scale without intelligent technology. However, in large organizations, learning happens all the time in various modalities. Personalization has to be supported with technology that can make it happen for everyone. An adaptive learning solution integrates AI and machine learning to automate the personalization engine.
· The journey starts with understanding your learning strategy. L&D leaders must have a clear understanding of their organization’s goals and how learning helps accomplish them. This clarifies the approach to technology that best achieves the learning strategy and how that technology fits in the organization’s current learning tech ecosystem. No corporate learning infrastructure is the same—not because the technologies are different, but because the operating contexts are different.
To hear more about personalized learning at enterprise scale, tune into this episode of Brandon Hall Group’s Excellence at Work podcast with Manoj Kulkarni, CEO of Realizeit, BHG’s Michael Rochelle, and David Wentworth, or contact Realizeit to discuss your approach.