Older learning specifications, such as SCORM, focused heavily on formal training, which meant they painted limited pictures of learners. See how Experience API (xAPI) is being used to track and support all learning experiences, including social interactions and on-the-job development.
xAPI & 70:20:10 Learning Model
When recorded via xAPI, social and work-based learning experiences offer many benefits. For example, learners have full records of their learning experiences, which they can use for future reference and recognition. This also means learners may be able to skip formal training on topics they’ve covered via informal learning.
Furthermore, as an L&D practitioner, you now have a clearer understanding of where learning is taking place and are better able to determine potential gaps in your learning programs. This also means you can more precisely evaluate the impact of social and workplace learning as it pertains to different areas of learning. For instance, you can encourage areas of positive learning while tackling negative learning (i.e., when people are learning to do the wrong things from a peer, colleague, or coworker).
Lastly, xAPI removes the boundary between informal learning and formal training programs, giving you the opportunity to more easily blend social and work-based learning into formal training programs.
In a Nutshell: The 70:20:10 Method
70:20:10 is a learning model that says we learn 70% through working or simply doing our jobs; 20% through conversations with peers; and 10% through formal training.
- The model is based on survey of 200 managers in the ‘90s and has risen to global popularity.
During the last 20 years, the internet has become a significant resource for self-directed learning.
Workplace and social learning shouldn’t be forced, but you can improve how information is shared.
xAPI makes tracking informal learning easier, giving much-needed intelligence that can help save your organization time and money.
Don’t miss out!
This week concludes our discussion of the 70:20:10 learning model in our ongoing series on Distributed Learning. Join us next time, as we cover microlearning, which targets a single learning objective in a short period of time.
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