Using human and artificial intelligence techniques to makes learning recommendations
In this session, we’ll look at the full spectrum of personalization from colours, avatars and themes to algorithmic and social personalization. We’ll then look at what kinds of personalization are useful to learning experiences today. I’ll argue that recommendations are the biggest ticket item. With less than five minutes available each day, it is imperative that the busy, overwhelmed modern worker uses that limited time optimally. She won’t have the time or the inclination to search for the material. So intelligent, algorithmic recommendations may be the only solution, as they have turned out to be in adjacent fields such as music (Spotify), video (YouTube), news (Twitter) and information (Google). But making recommendations in learning is a wicked problem, fraught with low data, unsticky content, and benefits which are hard to measure and felt over the long term. I’ll point out some of the ways Filtered Technologies and others go about solving it.
Marc Zao-Sanders is CEO and co-Founder at Filtered Technologies which uses a combination of human and artificial intelligence techniques to make learning recommendations to the workforce. Marc started his career in strategy consulting, then founded a couple of other education organisations before co-founding Filtered.