E-Learning 3.0: Conversation With Ben Werdmuller
jennymackness,
e-learning 3.0 – Jenny Connected,
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E-Learning 3.0 Newsletter
Newsletter archives are here
Concluding Graph
Videos
E-Learning 3.0 - Graph Nov 10, 2018 video 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. URL: https://www.youtube.com/watch?v=WiaxHxiN_IA
Articles
E-Learning 3.0, Part 3: Graph
Stephen Downes,
2018/11/10
Posts
The Unseen Cloud And The Nature Of Learning
dogtrax,
EL30 – Kevin's Meandering Mind,
2018/11/10
Web: [Direct Link] [This Post]
A Comic Reaction To The Data Visualization
dogtrax,
EL30 – Kevin's Meandering Mind,
2018/11/10
Web: [Direct Link] [This Post]
#El30 Technical: How I Created My Task
x28,
EL30 – x28's new Blog,
2018/11/10
Continue reading →
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#EL30 Graph task | x28's new Blog
x28,
EL30 – x28's new Blog,
2018/11/10
Continue reading →
Web: [Direct Link] [This Post]
Graph #El30 Week 3
Laura,
lauraritchie.com,
2018/11/10
Stephen has tasked us all with creating a graph of some sort for #el30 this week. Questions that came into my mind were: What are the parameters? How do they interact? How can I make visible the potentials? I’m thinking of learning and what’s visible, what we bring, resources, and what is ideal to implement […] The post Graph #el30 Week 3 appeared first on lauraritchie.com.
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Task
This week's task has three parts
- Create a model graph of some aspect of the E-Learning 3.0 course (it doesn't have to be an actual graph, only a representation of what an actual graph might look like. We've already seen, eg., graphs on the relations between people in the course. Could there be other types of graphs?
- In your model, consider how the states of the entities in that graph might vary. Consider not only how nodes might vary (eg., a person might have a different height over time) but also how the edges might vary (eg., a person might have a different strength of relation (calculated how?) with another person over time).
- In your model, consider how knowledge about the changes in states in the graph might be used.