# Getting Started With Gephi Network Visualisation App – My Facebook Network, Part III: Ego Filters and Simple Network Stats

In a couple of previous posts on exploring my Facebook network with Gephi, I’ve shown how to plot visualise the network, and how to start constructing various filtered views over it (Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part I and Getting Started With Gephi Network Visualisation App – My Facebook Network, Part II: Basic Filters). In this post, I’ll explore a new feature, ego filters, as well as looking at some simple social network analysis tools that can help us better understand the structure of a social network.

To start with, I’m going to load my Facebook network data (grabbed via the Netvizz app, as before) into Gephi as an undirected graph. As mentioned above, the ego network filter is a new addition to Gephi, which will show that part of a graph that is connected to a particular person. So for example, I can apply the ego filter (from the Topology folder in the list of filters) to “George Siemens” to see which of my Facebook friends George knows.

If I save this as a workspace, I can then tunnel into it a little more, for example by applying a new ego filter to the subgraph of my friends who George Siemens knows. In this case, lets add Grainne to the mix – and see who of my friends know both George Siemens and Grainne:

Note that I could have achieved a similar effect with the full graph by using the intersection filter (as introduced in the previous post in this series):

The depth of the ego filter also allows you to see who of of my friends the named individual knows either directly, or through one of my other friends. Using an ego filtered network to depth two (frined of a friend) around George Siemens, I can run some network statistics over just that group of people. So for example, if I run the Degree statistics over the network, and then set the node size according to node degree within that network this is what I get:

(I also turned node labels on and set their size proportional to node size.)

Running Network Diameter stats generates the following sorts of report:

That is:

– betweenness centrality;
– closeness centrality;
– eccentricity.

These all sound pretty technical, so what do they refer to?

Betweenness centrality is a measure based on the number of shortest paths between any two nodes that pass through a particular node. Nodes around the edge of the network would typically have a low betweenness centrality. A high betweenness centrality might suggest that the individual is connecting various different parts of the network together.

Closeness centrality is a measure that indicates how close a node is to all the other nodes in a network, whether or not the node lays on a shortest path between other nodes. A high closeness centrality means that there is a large average distance to other nodes in the network. (So a small closeness centrality means there is a short average distance to all other nodes in the network. Geddit? (I think sometimes the reciprocal of this measure is given as closeness centrality:-).

The eccentricity measure captures the distance between a node and the node that is furthest from it; so a high eccentricity means that the furthest away node in the network is a long way away, and a low eccentricity means that the furthest away node is actually quite close.

So let’s have a look at the structure of my Facebook network, as filtered according to George’s ego filter, depth 2:

Plotting size proportional to betweenness centrality, we see Martin Weller, Grainne and Stephen Downes are influential in keeping different parts of my network connected:

As far as outliers go, we can look at the closeness centrality and eccentricity (to protect the innocent, I shall suppress the names!)

Here, the colour field defines the closeness centrality and the size of the node the eccentricity. It’s quite easy to identify the people in this network who are not well connected and who are unlikely to be able to reach each other easily through those of my friends they know.

From nods with similar sizes and different colours, we also see how it’s quite possible for two nodes to have a similar eccentricity (similar distances to the furthest away nodes) and very different closeness centrality (that is, the node may have a small or large average distance to every other node in the graph). For example, if a node is connected to a very well connected node, it will lower the closeness centrality.

So for example, if we look at the ego network with the above netwrok based around the very well connected Martin Weller, what do we see?

The colder, blue shaded circles (high closeness centrality) have disappeared. Being a Martin Weller friend (in my Facebook network at least) has the effect of lowering your closeness centrality, i.e. bringing you closer to all the other people in the network.

Okay, that’s definitely more than enough for now. Why not have a play looking at your Facebook network, and seeing if you can identify who the best connected folk are?

PS when plotting charts, I think Gephi uses data from the last statistics run it did, even if that was in another workspace, so it’s always worth running the statistics over the current graph if you intend to chart something based on those stats…

# Why I Joined the Facebook Privacy Changes Backlash…

Whenever Facebook rolls out a major change, there’s a backlash… Here’s why I posted recently about how to opt out of Facebook’s new services…

Firstly, I’m quite happy to admit that it might be that you will be benefit from opting in to the Facebook personaliation and behavioural targeting services. If you take the line that better targeted ads are content, and behavioural advertising is one way to achieve that, all well and good. Just bear in mind that your purchasing decisions will be even more directedly influenced ;-)

What does concern me is that part of the attraction of Facebook for many people are its privacy controls. But when they’re too confusing to understand, and potentially misleading, it’s a Bad Thing… (I suppose you could argue that Facebook is innovating in terms of privacy, openness, and data sharing on behalf of its users, but is that a Good Thing?)

If folk think they have set their privacy setting one way, but they operate in another through the myriad interactions of the different settings, users may find that the images and updates they think they are posting into a closed garden, are in fact being made public in other ways, whether by the actions of their friends, applications they have installed, pages they have connected to, or websites they visit.

The Facebook privacy settings also seem to me to suggest various asymmetries. For example, if think I am only sharing videos with friends, then if those friends can also share on content because of the way I have set/not changed the default on another setting, I may be publishing content in a way that was not intended. It seems to me that Facebook is set up to devolve trust to the edge of my network – I publish to the edge of the my network, for example, but the people or pages on the edge of my network can then push the content out further.

So for example, in the case of connecting to pages, Facebook says: “Keep in mind that Facebook Pages you connect to are public. You can control which friends are able to see connections listed on your profile, but you may still show up on Pages you’re connected to. If you don’t want to show up on those Pages, simply disconnect from them by clicking the “Unlike” link in the bottom left column of the Page.”

The privacy settings around how friends can share on content I have shared with them is also confusing – do their privacy settings override mine on content I have published to them?

I’m starting to think (and maybe I’m wrong on this) that the best way of thinking about Facebook is to assume that everything you publish to your Facebook network can be republished by the members of your network under the terms of their privacy conditions. So if I publish a photo that you can see, then I have to assume that you can also publish it under your privacy settings. And so on…

This contrasts with a view of each object having a privacy setting, and that by publishing an object, the publisher controls that setting. So for example, I could publish an object and say it could only be seen by friends of me, and that setting would stick with the object. If you treid to republish it, it could only be repulshed to your friends who are also my friends. My privacy settings would set the scope, or maximum reach, of your republication of it.

Regular readers will know I’ve started looking at ways of visualising Facebook networks using Gephi. What I’m starting to think is that Facebook should offer a visualisation of the furthest reach of a person’s data, videos, images, updates, etc, given their current privacy settings (or preview changes to that reach if they want to test out new privacy settings.

PS re the visualisation thing – something like this, generated from your current settings, would do the job nicely:

More at The Evolution of Privacy on Facebook, including a view of just how open things are now…

It should look like this:

But WordPress saves it like this:

The WordPress saved version isn’t properly resolved by Facebook, it just goes to:

It should go to a page that looks like this:

Here’s a shortened link that does work: http://bit.ly/bwG9Xe

Follow it, and decide whether you like what you see. You do know who your friends are, don’t you, and you do know who they know? And where they go? And what applications they have installed? Becuase my reading of the above is that they can share information you shared with them to all those people, whether you approve or not? Or maybe I just misunderstand the permissions granted by the above form in the weird and wacky game of Top Trumps that is the Facebook privacy environment. Maybe the permissions you set to only share photos and videos with friends trumps the settings that let friends share your photos and videos with applications and sites they visit. Or maybe they don’t? Does anyone know for certain…?!

This is what mine looks like now:

For more on this, see: Keeping Up with Facebook Privacy Changes (Again)

PS You should probably also consider unchecking this ( http://www.facebook.com/settings/?tab=privacy&section=applications#!/settings/?tab=privacy&section=applications&field=instant_personalization ):

If you leave it set on Allow, when you visit a site that Facebook is friendly with it might share you data with that partner site for you… bless…

PPS Because Facebook is geting increasingly cavalier with what it lets applications do with you data, I suggest you take a look at the applications you have installed from the Applications page at:

This page is not easy to find from the under the privacy settings, but can be reached from the Facebook Account menu, under Application Settings.

If you don’t use an app, particularly an external one, I suggest you delete it…

# Getting Started With Gephi Network Visualisation App – My Facebook Network, Part II: Basic Filters

In Getting Started With Gephi Network Visualisation App – My Facebook Network, Part I I described how to get up and running with the Gephi network visualisation tool using social graph data pulled out of my Facebook account. In this post, I’ll explore some of the tools that Gephi provides for exploring a network in a more structured way.

If you aren’t familiar with Gephi, and if you haven’t read Part I of this series, I suggest you do so now…

…done that…?

Okay, so where do we begin? As before, I’m going to start with a fresh worksheet, and load my Facebook network data, downloaded via the netvizz app, into Gephi, but as an undirected graph this time! So far, so exactly the same as last time. Just to give me some pointers over the graph, I’m going to set the node size to be proportional to the degree of each node (that is, the number of people each person is connected to).

I can activate text labels for the nodes that are proportional to the node sizes from the toolbar along the bottom of the Graph panel:

…remembering to turn on the text labels, of course!

So – how can we explore the data visually using Gephi? One way is to use filters. The notion of filtering is incredibly powerful one, and one that I think is both often assumed and underestimated, so let’s just have a quick recap on what filtering is all about.

This maybe?

[“green beans” by House Of Sims]

Filters – such as sieves, or colanders, but also like EQ settings and graphic, bass or treble equalisers on music players, colour filters on cameras and so on – are things that can be used to separate one thing from another based on their different properties. So for example, a colander can be used to separate green beans from the water it was boiled in, and a bass filter can be used to filter out the low frequency pounding of the bass on an audio music track. In Gephi, we can use filters to separate out parts of a network that have particular properties from other parts of the network.

The graph of Facebook friends that we’re looking at shows people I know as nodes; a line connecting two nodes (generally known as an edge) shows that that the two people represented by the corresponding nodes are also friends with each other. The size of the node depicts its degree, that is, the number of edges that are connected to it. We might interpret this as the popularity (or at least, the connectedness) of a particular person in my Facebook network, as determined by the number of my friends that they are also a friend of.

(In an undirected network like Facebook, where if A is a friend of B, B is also a friend of A, the edges are simple lines. In a directed network, such as the social graph provided by Twitter, the edges have a direction, and are typically represented by arrows. The arrow shows the direction of the relationship defined by the edge, so in Twitter an arrow going from A to B might represent that A is a follower of B; but if there is no second arrow going from B to A, then B is not following A.)

We’ve already used degree property of the nodes to scale the size of the nodes as depicted in the network graph window. But we can also use this property to filter the graph, and see just who the most (or least) connected members of my Facebook friends are. That is, we can see which people are friends of lots of the people am I friends of.

So for example – of my Facebook friends, which of them are friends of at least 35 people I am friends with? In the Filter panel, click on the Degree Range element in the Topology folder in the Filter panel Library and drag and drop it on to the Drag Filter Here

Adjust the Degree Range settings slider and hit the Filter button. The changes to allow us to see different views over the network corresponding to number of connections. So for example, in the view shown above, we can see members of my Facebook network who are friends with at least 30 other friends in my network. In my case, the best connected are work colleagues.

Going the other way, we can see who is not well connected:

One of the nice things we can do with Gephi is use the filters to create new graphs to work with, using the notion of workspaces.

If I export the graph of people in my network with more than 35 connections, it is place into a nw workspace, where I can work on it separately from the complete graph.

Navigating between workspaces is achieved via a controller in the status bar at the bottom right of the Gephi environment:

The new workspace contains just the nodes that had 35 or more connections in the original graph. (I’m not sure if we can rename, or add description information, to the workspace? If you know how to do this, please add a comment to the post saying how:-)

If we go back to the original graph, we can now delete the filter (right click, delete) and see the whole network again.

One very powerful filter rule that it’s worth getting to grips with is the Union filter. This allows you to view nodes (and the connections between them) of different filtered views of the graph that might otherwise be disjoint. So for example, if I want to look at members of my network with ten or less connections, but also see how they connect to each other to Martin Weller, who has over 60 connections, the Union filter is the way to do it:

That is, the Union filter will display all nodes, and the connections between them, that either have 10 or less connections, or 60 or more connections.

As before, I can save just the members of this subnetwork to a new workspace, and save the whole project from the File menu in the normal way.

Okay, that’s enough for now… have a play with some of the other filter options, and paste a comment back here about any that look like they might be interesting. For example, can you find a way of displaying just the people who are connected to Martin Weller?

# Keeping Up with Facebook Privacy Changes (Again)

Although on a day to day basis I’m a Mac user, every so often I need to dip into the Windows virtual machine on my laptop. This generally fills me with fear and trepidation, because as an infrequent Windows user, whenever I do go over to the dark side I know my internet connection will grind to a halt and I will get regular requests to restart the machine as Windows goes into update mode. In a similar vein, on a day to day basis it’s Twitter that meets my social web needs. But on the rare occasions I go into Facebook, I’m also filled with dread. Why? Because there is frequently a new privacy minefield to negotiate (e.g. Keeping Your Facebook Updates Private).

Over the last few days, there’s been a Facebook developers conference, so I thought it worth checking in to see what new horrors have been released; and here’s what I saw today:

Hmm…

Ah – according to Deceiving Users with the Facebook Like Button, it appears that “Removing the feed item from your newsfeed does not remove your like — it stays in your profile. You have to click the button again to remove the ‘Like’ relationship.” So it could be used as a social bookmarking service, of a sort. Or at least a Facebook equivalent to “favorited” websites in your browser.

As you might have guessed from the previously linked to post, all may not (yet) be well with the Liked implementation though – because it seems that it’s possible to add a “Like” link on one page that actually Likes a page on another website. Which reminds me a little of phishing

So, what other goodness (?!) does Facebook have in store for us?

Instant personalisation, hmm…? So if I go to Pandora, say, it can trawl my Facebook profile, decide from my Likes and updates that I’m a goth hippy groover, and generate a personalised radio station for me jus’ like that? The oo’s have it… (ooh…, cool… or spooky…?;-)

And guess what, Facebook have thoughtfully opted me in to that service, without me having to do anything, and without even forcing me to notice (I didn’t have to follow the link on the home page to read the new service announcement; and for mobile users, I wonder if any of the Facebook apps tell the users that they’ve been opted in to this new way of giving their personal data to third parties…?)

I think I’ll untick…

Am I sure…? Err, yes… Confirm.

But what does this mean…?

Please keep in mind that if you opt out, your friends may still share public Facebook information about you to personalize their experience on these partner sites unless you block the application.

Hmmm, I think I’ll Learn More… (do you ever get the feeling you’ve ended up in one of those Create Your Own Adventure style games, only for real… Is this Brazil, or a Trial?

Err, right…

I guess this is the one:

What data is shared with instantly personalized partner sites?
When you and your friends visit an instantly personalized site, the partner can use your public Facebook information, which includes your name, profile picture, gender, and connections. To access any non-public information, the website is required to ask for you or your friend’s explicit permission.

Or is that “When you or your friends visit…”? That is, if my friend visits Pandora and goes for instant personalisation, can Pandora use my friend as a vector to grab my public information? A question that now follows is – can Pandora identify my Facebook identity through some mechanism or other (e.g. Facebook set cookies?) and reconcile that with what it has learned about me from my friends who have opted in to personlisation features. And if so, could it then offer me personalisation services anyway, even though I opted out on Facebook…?

I’m still unticking… because as Facebook adds partners, I probably won’t pick up on it…

So, do I dare walk up the Facebook Privacy tree…? Let’s go up to the Privacy Setting page:

So here’s the Profile Settings control panel:

Hmmm… there’s a link there to Application Settings, which I don’t think appears on the Privacy settings page. Where does it go?

I’m not sure I understand everything in that drop down menu…?

Search?

Blocking?

Sheesh.. So here are the tabs that I have to work through:

Many of the pages only require setting a simple drop down box (though thinking through the implications, and what relates to what may be comples); but there are also quite a few that offer “Edit Settings…” links, and I suspect that some of those open up into rather more involved dialogues…

I reckon you could easily spend at least 1 week/10 hours of a 10 point short course just looking at Facebook privacy settings, and trying to think through what the implications are…

Which brings to mind the Facebook network visulisation I started working on with Gephi… Could we use visualisation tools to highlight who in your Facebook network can see what given your current privacy settings? Methinks there’s an app in that…

PS popping back in to Facebook just now to delete most of the apps I’m signed up to, I noticed on the “click here” page linked to above the option:

Control what your friends can share about you when using applications and websites

Clicking through to Edit Settings, here’s what I see:

[Since grabbing that screenshot, I’ve unchecked all those boxes…]

I’ll spell out the text for you:

When your friend visits a Facebook-enhanced application or website, they may want to share certain information to make the experience more social. For example, a greeting card application may use your birthday information to prompt your friend to send a card

If your friend uses an application that you do not use, you can control what types of information the application can access. Please note that applications will always be able to access your publicly available information (Name, Profile Picture, Gender, Current City, Networks, Friend List, and Pages) and information that is visible to Everyone

So… if i don’t take steps to protect my information, then my friends can give access to my presence, videos, links, photos, videos and photos and tagged in, my birthday, hometown etc etc to third party applications? Does that mean if I have various privacy settings set to share with friends only, they can still share the information on to third parties I did not anticipate seeing the data?

In the following set up, who can see photos and videos of me?

Answers in the comments please… If anyone’s done the experiments to see just how the various previous setting inter-relate, I’d love to see a write-up. I’m also thinking: maybe Facebook should be required to publish a logical model of what’s going on? (Are there logics of privacy? You could probably get somewhere close using epistemic logic?)

(It’s all a bit like writing legislation that says that as yet unspecified powers will be given to a Minister, who may then devolve those powers to others…;-)

PPS a page I didn’t link to/show a screengrab of but should have included is the Applications page (this is not under the privacy settings. You can find it here: http://www.facebook.com/#!/editapps.php?v=allowed

If you don’t use an app, particularly an external one, I suggest you delete it…

# Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part I

A couple of weeks ago, I came across Gephi, a desktop application for visualising networks.

And quite by chance, a day or two after I was asked about any tools I knew of that could visualise and help analyse social network activity around an OU course… which I take as a reasonable justification for exploring exactly what Gephi can do :-)

So, after a few false starts, here’s what I’ve learned so far…

First up, we need to get some graph data – netvizz – facebook to gephi suggests that the netvizz facebook app can be used to grab a copy of your Facebook network in a format that Gephi understands, so I installed the app, downloaded my network file, and then uninstalled the app… (can’t be too careful ;-)

Once Gephi is launched (and updated, if it’s a new download – you’ll see an updates prompt in the status bar along the bottom of the Gephi window, right hand side) Open… the network file you downloaded.

NB I think the graph should probably be loaded as an undirected graph… That is, if A connects to B, B connects to A. But I’m committed to the directed version in this case, so we’ll stick with it… (The directed version would make sense for a Twitter network (which has an asymmetric friending model), where A may follow B, but B might choose not to follow A. In Facebook, friending is symmetric – A can only friend B if B friends A.

(Btw, I’ve come across a few gotchas using Gephi so far, including losing the window layout shown above. Playing with the Reset Windows from the Windows menu sometimes helps… There may be an easier way, but I haven’t found it yet…)

The graph window gives a preview of the network – in this case, the nodes are people and the edges show that one person is following another. (Remember, I should have loaded this as an undirected graph. The directed edges are just an artefact of the way the edge list that states who is connected to whom was generated by netvizz.)

Using the scroll wheel on a mouse (or two finger push on my Mac mousepad), you can zoom in and out of the network in the graph view. You can also move nodes around, view the labels, switch the edges on and off off, and recenter the view.

Not shown – but possible – is deleting nodes from the graph, as well as editing their properties.

You can also generate views of the graph that show information about the network. In the Ranking panel, if you select the Nodes tab, set the option to Degree (the number of edges/connections attached to a node) and then choose the node size button (the jewel), you can set the size of the node to be proportional to the number of connections. Tune the min and max sizes as required, then hit apply:

You can also colour the nodes according to properties:

So for example, we might get something like this:

Label size and colour can also be proportional to node attributes:

To view the labels, make sure you click on the Text labels option at the bottom of the graph panel. You may also need to tweak the label size slider that’s also on the bottom of the panel.

If you want to generate a pretty version of the graph, you need to do a couple of things. Firstly, in the layout panel, select a layout algorithm. Force Atlas is the one that the original tutorial recommends. The repulsion strength determines how dispersed the final graph will be (i.e. it sets the “repulsive force” between nodes); I set a value of 2000, but feel free to play:

When you hit Run, the button label will change to Stop and the graph should start to move and reorganise itself. Hit Stop when the graph looks a little better laid out. Remember, you can also move nodes around in the graph as show in the video above.

Having run the Layout routine, we can now generate a pretty view of the graph. In the Preview Settings panel on the left-hand side of the Gephi environment, select “Show Labels” and then hit “Refresh”:

In the Preview panel, (next tab along from Preview Settings), you should see a the prettified, 3D layout view:

Note that in this case I haven’t made much attempt at generating a nice layout, for example by moving nodes around in the graph window to better position them, but you can do… (just remember to Refresh the Preview view in the Preview Settings… (There must be a shortcut way of doing that, but I haven’t found it…!:-(

If you want to look at who any particular individual is connected to, you can go to the
Data Table panel (again in the set of panels on the right hand side, just along from the Preview tab panel) and search for people by name. Here, I’m searching the edges to see who of my Facebook friends a certain Martin W is also connected to on Facebook;

It’s easy enough to highlight/select and copy these cells and then post them into a spreadsheet if required.

So that’s step 1 of getting started with Gephi… a way of using it to explore a graph in very general terms; but that’s not where the real fun lies. That starts when you start processing the graph by running statistics and filters over it. But for that, you’ll have to wait for the next post in this series… which is here: Getting Started With Gephi Network Visualisation App – My Facebook Network, Part II: Basic Filters

I was at a meeting yesterday looking at rebooting the OU’s Facebook strategy. With a bit of luck, this means that we’ll be doing another push on the OU Facebook apps that were developed several years ago now and which I still believe provide a sound basis for a range of community building and social learning support services (Course Profiles – A Facebook Application for Open University Students and Alumni).

The apps were largely developed out of time and in stolen time, and it seems that things are likely to continue in this way (which is both a plus – freeing us from constraints of interminable committees wanting to plan strategies rather than jfdi, and a minus – @liamgh is the only person we trust with the code which means any maintenance falls to him ;-)

For those who don’t remember the apps we developed, there were two: Course Profiles, which allowed students to declare the courses that had taken were taking and intended to take, and then provided a range of services around that information (find friends on a course, find a study buddy, link to course information or course related OpenLearn resources, get course recommendations); and My OU Story, where students could maintain a “status diary” about their progress on a course, along with a mood indicator so they could track their mood over a course, and other app users could add supportive comments. (I’d be surprised if anyone in the Student Services retention project has even heard about this project, but looking at some of the peer support that has gone on within the context that app, I’d argue it might be contributing to retention…)

Course Profiles quickly attracted several thousand users following the initial push just after it was first launched, so it evidently served a need then that presumably still exists today, i.e. a badging mechansims for celebrating course achievements and declaring future study intentions. One thing that might be worth looking at is the rate at which early adopters of Course Profiles have continued to update it, and report on the extent to which their original “future study” intentions converted to actual course registrations.

There’s also going to be a push on growing the number of fans on the official OU profile page. I’m not sure what plan @stuartbrown has for growing the numbers (for the task appears to have fallen to him…;-) but with a bit of luck the apps as well as the fan page will get highlighted through some of the official communication channels.

We also had a bit of discussion around other potential apps. Something I’d quite like to see would be a gallery app pulling images from the various flickr groups that have popped up around the T189 Digital Photography short course. Alumni of that group are already pretty active, and have just launched their first online exhibition, so if we could provide a channel that increases the audience for their show, and if they’re happy for us to amplify it via an OU Facebook app, that might be quite a fun thing to try as a community building app… (For more about the background to the exhibition, see Inspiring Learners; also see the T189 Graduates’ Exhibition).

(I also wonder if a similar gallery style app might work to showcase some of the games that students on T151 Digital worlds manage to create, all with their permission of course…)

Someone (I forget who) also suggested a “Share on Facebook” button within the gallery environment students use to build their portfolio whilst they take T189 (limited so that sharing was limited to photographs that a student had uploaded themselves, of course). This would amplify a student’s work and progress on a course to their Facebook friends, and provide their friends with a glimpse of what sorts of activities are involved in this particular OU course.

One thing I never even half managed to convince anybody that it was important was the data that was collected by the Course Profiles app in particular, though I did have a go at a few quick’n’dirty takes on this, such as OU Course Profiles Facebook App – Treemaps, Hierarchical Course Clusters from Course Profiles App and Tinkering with Google Charts (which started to consider what a course team dashboard view might look like). I was mulling this over again last night, and the following uses came to mind if we started to reconcile Course Profiles with institutional data (something we were always wary of, but anyway – here’s the thinking…;-)

– predictors and conversion rates: I’m not sure if Liam is logging when/how users change their status updates, but it’d be useful to know what percentage of users are updating their Course Profiles (e.g. from ‘currently taking’ to ‘taken’ courses, or more interestingly ‘intend to take’ to taking) and whether an “intend to take” course declaration is a good predictor of whether students do actually take a course. There’s an obvious quick win here for a possibly intrusive marketing campaign chasing folk who’ve declared an ‘intend to take’ course but don’t appear to have followed up on it;

– predicting course sizes: with several thousand users, does the sample of users on Course Profiles predict future course enrollment numbers? As far as I know, no-one in planning ever came to us asking to peak at our data to explore this. Nor did any more than a couple of Course Chairs ever seem to think it was interesting that we had stated intentions about course pathways, and that for new courses in particular we might be able to spot whether students were signing up for a course based on a pathway the course team was hoping for?

– retention: is the retention rate of students on a course who are on Facebook with Course Profiles and/or My OU Story different to the retention rate across the course as a whole? Does the fact that students who have declared ‘intend to take’ courses on the Course Profile correlate with their likelihood of completing an award?

– course planning and recommendation: on the one hand, courses appear to have natural numbers; on the other, working out what courses to take in what order for a particular degree given various factors (such as courses already taken, course exclusions etc) can be a confusing affair. At the moment, I believe a rule based support tool is being explored to help with course recommendations, but how well do those suggestions compare with a simple clustering based on Course Profiles data?

PS Just in passing, it’s worth noting that as with other groups who’ve used Facebook to mount campaigns against unpopular corporate decisions, OU students are no different… Open University curbs Tesco ‘clubcard degree’ scheme .