The rate at which the e-learning industry is growing speaks to significant changes in our world that have far-reaching effects socially and economically. These changes are rooted in technology, and their outcomes are determined by the speed with which we address their cultural impact.
At DevLearn this year, Eli Pariser, an Internet activist and author of The Filter Bubble, is slated to talk about the unanticipated consequences of tailoring and customizing online experiences. The “filter bubble” forms when useful and relevant information is omitted from us based on who and what we like online – from Google search results that are customized based on prior browsing activity, to online suggestions based on our Facebook connections, to Netflix recommendations based on what we’ve watched before. We each live in our own filter bubble. And as the filter bubble builds, the spread of important ideas diminishes.
The filter bubble can impact how and what we learn. As Millennials enter the workplace, their expectations for customized and personal experiences place increasing pressures on those who must deliver training in this new modernity. Social learning environments and social learning solutions become more prevalent as learning departments glean from other areas of the organization the advantages of community building, social influence and the seeding of content to stage messaging. The trick is to create means of training that address customization without sacrificing the spread of important information.
In 2011, around the time of Pariser’s Ted Talk and the publication of The Filter Bubble, Klout, a service that measures influence across the social Web, changed its scoring method. The service’s well-established and well-understood algorithm was changed to reflect a growing number of daily social signals across a larger number of social channels. Many users saw their Klout scores drop dramatically after having worked hard to increase them by building influence. Overnight, Klout changed the way it measured influence.
Having just read The Filter Bubble, I became curious about just how I was being influenced by the social Web. My article, “Social Greed: Influence Is What’s Wrong With Social Media,” was shared by a few key influencers, and within days, Klout’s founder candidly addressed each of my concerns. Influence, it was obvious, can unfavorably alter how ideas flow.
As we create online environments for collaborative learning, it’s important that we structure them so that social greed does not drive the messaging. Organizational flatness needs to be promoted in a collaborative learning environment so that good ideas spread and bad ones fizzle out on their own merit, not because of a hierarchical, pyramided structure of influence.
So how do we keep the filter bubble from forming in collaborative learning environments at work? The challenge comes in maintaining alignment of goals and objectives between the organization and the individual as the flow of ideas is encouraged. Issues of governance, monitoring and moderating arise as the organization begins to actively use many of the collaborative features of, say, their SharePoint.
New technologies are emerging that help overcome this challenge. Take the Experience API, for instance, aka Tin Can. This learning technology specification gives us the ability, for the first time, to track learner activity in practically any environment, even ones that are offline. From a formal module to proprietary sales software to a WordPress blog to a classroom, the Experience API tracks practically any activity – clicks, likes, shares, ratings, comments, assessments, video views – and reports that data in any customizable fashion. So the organization is able to monitor this free flow of ideas and learn where and when learners are learning. Or not learning. And refine accordingly.
Enterprise equivalents of content discovery and publishing platforms like Flipboard, Scoop.it and Feedly can also help. Platforms like these are built with the understanding that algorithms alone aren’t enough to organize the Web, and so they combine big data technology with user curiosity to allow users to curate relevant and important ideas. Sure, platforms like these can filter suggestions through an individual’s social graph (friends, followers and connections), but most offer tools like suggestion engines, bookmarklets, and even curator communities that empower individuals to reach beyond the limitations of their social graph, helping to avoid the filter bubble.
As the number of collaborative learning environments increases, it’s important we keep them bubble free. I’m looking forward to Eli Pariser’s keynote and his thoughts on how we can adapt his ideas to learning.