Learning Recommendations in the Age of Curation

In 2017, talent and learning leaders will begin to embrace the world of “Learning Recommendations,” an organic extension of our organizations’ desire to curate content and our employees’ need for support in making good content and learning choices.

The learner is thrilled to have an ever-widening “panorama” of learning choices, including formal content from the organization, user-created content, social networking connections to internal expertise, open content (e.g., TED Talks and YouTube), and more. But, the learner is facing that panorama without the tools—or even the time to make appropriate and personal choices. 

The learner wants recommendations that combine emphasis from their management, ratings from peers, and data from the LMS about overall use and how content fits with their pattern of consumption. In the near future, recommendations from a machine learning function will both expand the range of the panorama and offer more precise and personalized advice to the learner.

Recommendation functionality may appear as an extension of talent, learning, or knowledge systems. It may exist as an added layer, interfacing with these enterprise systems, or it may pop up as a stand-alone app that will provide recommendations directly to the learner. We anticipate that external community recommendation layers will appear (e.g., adding learning advice from one’s fellow graduates of a MBA program).

For an organization to embrace recommendations, these shifts will be required:

• Curation strategies: A commitment needs to be made to organizing and providing access to a wider set of learning resources from all sources.
• Curation skills: Talent groups will need to add staff with curation skills, which often meld the functions of a new age librarian and content concierge.
• Freedom to explore: The learner will ask for greater freedom to explore content from sources that meet their needs, while still being held accountable for assessment of outcomes or readiness.
• Big and personal learning data capacity: Recommendations will focus our attention on a wider set of Big Data that is both enterprise wide and highly personal. For example, does this learner actually ever open a video link that we send to her? If so, does she finish watching the video?
• Excitement for community recommendations: The employee community needs to be empowered to help with honest and helpful crowd ratings. Colleagues should be free to say, “Watch this video, but start it at minute 3:05 where the best stuff begins and continues till minute 7:15.”

Recommendations are coming! They are the natural and organic extension of our desire to curate and harvest content from a wider knowledge set. Just as your employees want to see ratings for hotels or restaurants, they will want recommendations combined with trusted ratings and organizational support for targeted learning. 

Start with a pilot, pressure your learning systems suppliers to focus on recommendations, and partner with your learners to give them the tools they need to learn and develop in this expanding knowledge universe.

Elliott Masie is the Chair of The MASIE Center’s Learning CONSORTIUM and the host of Learning 2017.  www.masie.com

Read more 2017 talent predictions by other thought leaders.