Facilitator Stephen Downes of the course E-learning 3.0 (#el30) explains Graphs in this video. In his own words: The graph is the conceptual basis for web3 networks. A graph is a distributed representation of a state of affairs created by our interactions with each other. The graph is at once the outcome of these interactions […]

Facilitator Stephen Downes of the course E-learning 3.0 (#el30) explains Graphs in this video. In his own words:

The graph is the conceptual basis for web3 networks. A graph is a distributed representation of a state of affairs created by our interactions with each other. The graph is at once the outcome of these interactions and the source of truth about those states of affairs. The graph, properly constructed, is not merely a knowledge repository, but a perceptual system that draws on the individual experiences and contributions of each node. This informs not only what we learn, but how we learn.

Graph vs. storytelling

What interested me particularly is the idea that stuff like this website can be represented as a graph, which is fundamentally different from a representation as a linear narrative. Graphs enable a view from a variety of perspectives. In education we are drawn towards the narrative, the causal explanation, the single actor. There is a critique of this in the book How history gets things wrong by Alex Rosenberg. Professor Rosenberg (Duke University) demonstrates how our addiction to narratives gets in the way of understanding history. Graphs can be a corrective on this.

The question is whether narratives should by definition be linear. Cannot we tell stories with different paths depending on choices made by the people formerly known as the audience – making them active participants?

A demonstration of graphs can be found in this post by Laura Ritchie. It demonstrates that when we demonstrate our learning with a graph we change our perception of what it is we are learning and how we are learning. It changes our understanding of where the knowledge comes from. The essence is that everything depends on something else.

In GitHub you have cloning, versioning, merging, forking which are manipulations in a graph which lead to something new. Machine learning builds on the characteristics of graphs. Aggregating, remixing, repurposing are skills which define the new way to learn.

A graph or network is not just a place to store and manipulate data, it’s a perceptual system. Thinking and perceiving are one and the same state, so Stephen argues.