So can you measure a cMOOC? Or elsewhere and elsewhere too – well I suggest that xMOOCs can via the ever stalking Analytics monster can measure, or at least pertain to measure, which may be in honesty all we can claim to do. But we don’t need to have a more accurate measure to gauge a difference between a cMOOC or an xMOOC.

Let’s start with a bloodbath. The xMOOC is a coup d’etat – perhaps a coup d’education, as a new (often singular) educational presence rises in our lives and comes to become / offer / suggest a dominant voice. The cMOOC is a protest, people gathering to speak on a topic, often people you already know, or would already call on for help (see personal learning network – or “people I can ask” if you prefer to love the english language). So an xMOOC definitely exists, but a cMOOC is, in theory, a vast intersection of personal learning networks, or grids, or networks, or people. A cMOOC exists, but in a different form for different people. It remains an individual experience, and so with a lack of centralisation has no official metric or heuristic.

Give peace a chance! Imagine radio stations, the xMOOC is a single transmitter, the students radios. A radio station can find out how many listeners by counting the number of people with radios. The cMOOC is a world of small CBs or mobile phones, shooting messages round to each other with no hierarchy in transmitters. All speakers are equal, assumed or otherwise. The teacher / dominant xMOOC voice is assumed superior.

So the xMOOC network is a star network inherently reliant on the dominant voice – and the routes information can take are clearly defined as teacher -> student.

A cMOOC is a mesh network with no key person dependency in the middle, but with the routes information can take less precise, and perhaps also taking on messaging passing and organisational roles.

The mesh network is fine, if we assume node equality – say like this












Here 4 people are communicating knowledge (picture is deliberately like Zones of Proximal development). As each node is equal – the network is nicely isometric, and has no bias (there is a theory here about isometric crystals and piezoelectricity – which is basically the science of “sparks”). So a student looking to learn in this cMOOC can be anywhere and achieve a consistency experience.

We know however that the chance of a perfectly equal set of nodes is unlikely, so a network is more likely














Now, the network is not isometric. It has better places to be (towards the top left), so this node is more popular and that means is more influential, or does more work (or both).

Now this will naturally appear, even with consistent nodes, but if we “measure” – say by something as simple as highlighting a twitter chat diagram showing the major contributors would suggest he to go to to learn “better”. This is based on the idea people would place themselves nearer the “better” teachers, or people who are more outgoing and so communicate more.

So here, without a focus, or a dominant voice the network becomes skewed, or perhaps, because this is a flexible network, it has scope to reshape itself.

However if you measured the cMOOC / mesh (via something as simple as a twitter visualisation), then the network is altered. Allowing the measurement skews the network and alters the way it can behave. A bigger node is the slow movement from a cMOOC model to an xMOOC model (many cMOOCs seem to have figure heads or weekly experts – so this is probably already the case).

So, as a proposition, does measuring a cMOOC turn it into an xMOOC, and would measuring a cMOOC alter a cMOOC to make it less useful / organic? Measuring an xMOOC doesn’t alter the way the course behaves, measuring a cMOOC could. Schrodinger.

Also, the cMOOC argument is partially that cMOOCs scale, and for this to be true, then a mesh network is the likely topology for scaling to be true (the star would slow down as more links joined the central node). Measuring the cMOOC might indicate that although a mesh exists – it is perhaps more like a mesh linked into a series of smaller overlapping meshes, or a tree network with some mesh features – and neither of these structures can be as scalable as a mesh. Now these could be growing pains per say, but I think it is more the inevitable factor of communication. Websites that are useful are used more, people who are helpful help more. The scale argument which seems key, seems to rely on a mesh networking existing.

And if you think it doesn’t, imagine an anonymous cMOOC.

Neither network model scales by default however. Scale requires large broadcasts, which could be a committed mesh core (say, disciples) and then a series of similar events (say missionaries) . You could argue that scale is possible without relying on star networks, but the act of instigation is a star itself.