I think I get the idea of AI and neural networks and that a graph is an abstraction of human functions like the zoo of neural networks described in The Neural Network Zoo .
However, I was a bit stumped with the week’s task to create a model graph besides the social network graph that Stephen used as an example. Then I remembered something from Vaidehi Joshi’s A Gentle Introduction To Graph Theory . By reading her post I was literally on a graph and so the idea of taking the E-Learning 3.0 Newsletter Week 3: Graph and creating it as my model graph.
The states of the edges are rather linear except for theGrasshopper E-Learning 3.0 Logo graphic Child Node that leads to the Sibling Node which is also a Child Node on to the Root Node. The Direct Link Leaf Nodes are a mirror of the Resource or Post Child Nodes URL. The Course Newsletter/RSS Child Node Here is interesting in that it has the entire Page in a downloadable XML file.
It is a bit of stretch to discuss the model, in how knowledge changes in states in the graph might be used because of the linear nature of tree/graph. In fact, I don’t think it does.
But what I do think is that when you look at it from a design point of view it is rather consistent in design to Stephens OL Daily Newsletter not surprisingly.
The Resource and Post sections of the page with their Child Nodes have a similar format with the item hyperlink and then a curated description on its content followed by the same Leaf Nodes with the same functionality of the Direct Link. The Roor Node with the Newsletter archive link in design is the same as in OL Daily Year and date with the date hyperlinked to the OL Daily post.
The Sibling Nodes/Child Nodes with Leafs nodes is a nice design so that no matter where you are in the course that week you can find something you might want to go back to. It makes navigation through the site at any time during the course.
You can download an interactive model where the node links take you to the node’s connecting URL here –