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Google Knowledge Graph

First the web was a platform to help manage networks of documents and webpage files.  Then the web evolved into a platform to help us manage our social networks.  Today we see the web maturing into a serious platform for lifelong learning to help us better understand the world around us.

Among the more radical (and plausible) scenarios for the future of lifelong learning is that our  daily life experiences become reshaped by a learning graph that serves as visualized data record of what we know, how well we know it, where and who we learned it from and what we are trying to learn more about.

News organization could adapt their content with our learning graph to make us more informed.  Celebrity chefs might access our learning graph and deliver new experiences to teach us more about food.  Libraries might make book and course recommendations based on subjects that motivate us to learn more.  Work training programs might tap into our learning graphs to make sure our skills stay relevant and can be tied to real world business challenges.

The most idealized, techno-optimistic vision is a world where people become less focused on consuming and more demanding for experiences that support a lifetime of learning.  On the flip side, it does not take too much effort to imagine – or focus only on a dystopian future of learning graph abuse.

Today our social graphs shape our daily interactions with people, organizations and information flows. The next phase of our web-influenced culture will be teaching people the risks, rewards and responsibilities of managing a personal learning graph.

Bringing this vision of a learning graph influenced world will require us to go through the same transition as we saw with social norms confronting the era of social graphs.  At first people will likely be confused, indifferent or threatened by the very idea of knowing more about what they know.  Then we’ll enter a phase where people can say ‘how did we ever live without it’.

When Social Seemed Confusing and Creepy
Think way back to 2004 when the idea of a ‘social graph’ impacting our daily lives seemed like a confusing and creepy concept.  The early social graphs attempted to understand who I know and how I know them?

The information was structured in graph databases that are visualized by circles and lines. Circle nodes (e.g. people, concepts, places, resources) and lines that reveal relationships or types (e.g. is a friend, is a co-worker, ‘likes’).  Unlike traditional ‘row and column’ databases, graph databases are perfect for data+information that is rich in connections and relationships.

Ten years ago, nobody was begging for a social graph.  No organization thought about packaging its content to be compatible with social graph databases.  There were no algorithmic ‘bots’ designed to scrape social network data for sentiment and behavior.

Today, hundreds of millions of people across the world cannot get through a day without tapping their social graphs in Facebook, Twitter, LinkedIn, Match.com or some other equal.

Social graphs, for better and worse, influence the thoughts and stories we share and the content we receive.  Social graph databases are behind everything from crowd funding Kickstarter projects, Twitter ‘hash tag’ activism, to helping us find a new job or life partner.

All of this is possible because the social data is structured in graph databases made up of circles (nodes) and lines (edges) that make it easier to find connections, understand relationships and identify the right pathways to reaching a goal.

How about the learning graph?

Welcome to the (very) early days of the Learning Graph.

The learning graph is a visualized data record of what we know (which concepts and domains have we explored), how well we know it (where are we along the path of mastery), and where and who we learned it from (connection to people and resource collections).  It might also include hints of what we are trying to learn more about.

Today the learning graph is only an idea.  It is an idea with several names (e.g. Learning Record Store; Adaptive Learning Platforms) and potential ally technical specifications (e.g. ExperienceAPI, Mozilla Open Badges)

Yet it is a logical and plausible evolution based on today’s current direction of change in this networked society.

Here is a short list of assumptions to explore and learn more about.

#1 Graph Databases Play a Big Role 

Learning is about making connections between things.  It is about understanding relationships and abstractions.  How we structure data-information will matter.

The category of ‘graph databases’ (e.g. Neo4j, Titan) offers the most appealing foundation to integrating web content into a world of personal learning graphs.   The more content is structured around relationships, the more likely they will fit into the ecosystem of learning graphs.

Graph data-what?!

Think circles and lines! Circle nodes (e.g. people, concepts, places, resources) and lines that reveal relationships or types.

The circle (node) of Pablo Picasso would have a line connected (type of art movement) to another circle node of Cubism Art Movement.  From the Cubism Art Movement circle you would find many other lines to other artists from the movement.

The future of learning has more to do with our we structure our data than any devices that we use to learn.

Instead of ‘writing books’ or developing learning material content in simple text or video form, we might see all content providers structuring information in graph format so that they have more seamless integration with other connected database frameworks.

#2 Learning Graphs will Stumble if We Limit Implementation to ‘Schools’ 

Technology-led solutions for school-based learning always seem to run into challenges and constraints that limit the effectiveness of the tool.

A more desirable path for the learning graph might be found in lifelong learning that occurs via self-direct experiences, social learning communities or with civic institutions like libraries, museums and arts organizations.

Instead imagine focusing on lifelong learning.  Empowering likely early adopters like young people with learning graph experiences that simply capture their passion for learning – not taking a high stakes test.

Imagine a teenager inspired to learn more about street art.  Their learning graph might contain location of where they encountered a particular artist.  The graph might include the names of well-known artists and the connections between them in terms or style or cities.  The graph might include links to widely read books, blogs or Twitter feeds.  The graph might include connections of street artists to other art genres and artists that proceeded this movement.  The graph might include connections to social, cultural or political themes explored by artists.

In this visualized experience the teenager can jump from concept to concept – and at surface level realize the relationships between concepts, people and resources.  They can see at a glance how much there is to learn vs the concepts that they have attempted to learn about.

#3 Privacy & Data Ownership will be an Issue  

Digital Data. Privacy.

We know this story well.  Our digital lives have turned individuals into personal data factories.  Organizations (public-private sector) see enormous value in knowing more about our lives through access to personal data.  The notion of ‘privacy’ is being challenged at all levels.

Social norms and legal frameworks have not caught up.  This has not stopped the mainstream embrace of social graph experiences that go far beyond Facebook.

The way forward is to anticipate challenges and work to develop solutions.

Individuals working in the world of learning data and analytics are hoping to avoid a repeat of this missed opportunity to bring people and institutions up to speed on the risks, rewards and responsibilites of managing personal data that drives our lifelong learning.

There will likely be a push for ‘Own Your Own Data‘ policies around lifelong learning. There will also likely be a push for companies to ‘own your data’.  There will be learning graph data leaks and break-ins.

It is impossible to predict how it will unfold.  We cannot resolve this emerging issue.  People will have to stand up for their right to own their learning data.   Keep calm, Carry on.

Learn More….

Folks talking about learning graph – learning map related visions:

Danny Hillis – best known for a visual learning map (a concept likely built on top of a learning graph)
OSCON 2012

Danny Hillis, Applied Minds – 2012 talk

Jon Bischke (Twitter; LinkedIn; CEO of Entelo)  –
The Learning Graph & Reputation Graph (He references an earlier post by Kirstin Winkler)