Visualising networks of learning resources

I'm looking at the first dataset of bookmarks from MELT portal. Here you can see some of the first descriptions created by using Many Eyes. Click on the interact button in the pic and it loads. This is a treemap visualisation of the bookmarks that users so far have found.

What do you see here? You first see boxes in different colours. They are "boxed" by the user IDs. The bigger one is, the more learning resources this person has bookmarked. If you hoover your mouse over the boxes, you can see the ID of resources. These, of course, do not mean nothing to you now, but imagine if they were links to resources?

Next you can explore the data a bit further. Drag the mother tongue box on the top of the graph to the first place. Now, the boxes are displayed by the languages spoken by users. You'll see that Hungarian speakers have been busy on the portal, they have the most bookmarks.

Third, you can explore further by dragging the obj_lang to the first place. This shows the languages in which the bookmarked resources are. Interestingly, it turns out, most of these resources are in English. However, the diversity is there to be observed: users have found resources in many different languages useful.

Let's go further. The next one is a network diagram. If you click on "click to interact" you can also zoom into the visualisation.

What do you see here? It's a network that consist of: user mother tongue and the learning resource that those users bookmarked on the portal. You see 4 quite big vertices, which are the mother tongues of the users.

..network consists of a set of objects called vertices connected by edges. The visualization of the network is optimized to keep strongly related items in close proximity to each other. In this way, the overall arrangement of vertices in the network is very telling of the structure of the connections between vertices (vertices that are far away are weakly related to each other).In this visualization, the size of a vertex is proportional to the number of edges emanating from it.

Take the Hungarian speakers, for example. They are the ones who user the portal most, and have actually bookmarked a fair amount of resource. At the end of each edge you can see an ID number. Those are the ID of learning resources that these teachers have bookmarked. The same goes for Finnish speakers, Dutch speakers, etc.

Interestingly, we can see from this visualisation that not many resources are shared among the users from different language groups. A few are, though: take, for example, the LeMill resource that is visualised in orange in the image here. It has edges linking it to Finnish, German and Hungarian speakers. I counted 14 resources in this small dataset that were shared by users from different countries, that's about 13% of resources.

This type of resources are what we call "travel well" resources, as they can cross borders. In this case those borders are lingual. The resource also acts as a bridge between these different language communities. If you look at the resource in question, you'll find that it is to teach English (as
foreign language) and it is in English. Thus, it is not that surprising that it is well accepted in many language communities.

Finally, I also visualised the languages of learning resources instead of the resource ID. You can find it here. As you see from the image on the right, I have highlighted the languages of resources from Dutch speaking users. They have been pretty busy finding resources in all kinds of languages!