In this installment of our Distributed Learning series, find out how you can use L&D data to support self-directed learning across your organization.
Why is self-directed learning important?
Self-directed learning occurs when learners take the initiative to find resources and learn something for themselves using resources such as Google, Wikipedia, and YouTube.
So, why is this type of learning important? Because, a large part of what people learn in the modern world isn’t from the 70:20:10 elements (i.e., formal, social, and on-the-job) as defined 20 years ago, but from the internet.
Reinforce self-directed learning with data.
You can use learning data to support and add recommendations. Degreed is a great example of a platform that captures data about individual learning experiences and uses that data to recommend similar learning experiences. These recommendations are based entirely on a learner’s self-directed learning. (It’s similar to Amazon’s “people who bought this also bought” feature, but for learning programs.)
As an L&D practitioner, you can integrate this type of data from Degreed—or similar platforms—with a learning record store to recommend specific content based on a much broader field of data.
For instance, you can make even more accurate recommendations by factoring in data about a person’s recent workplace formal training, achievements, or social interactions. By providing these suggestions, you help ensure learners quickly and easily find the content and resources that are most relevant to their roles and experiences.
Up Next: How to Prevent Negative Learning
All learning is good, right? Find out in our next post, as we discuss how you can use data to pinpoint and manage negative learning within your organization.