Experimenting with the Learning Registry
This post is my reflections on the emerging conclusions from the JLeRN Experiment.
Applying a new approach to an old problem
Followers of technology trends will have noticed some of the big themes of recent years include cloud storage, big data, analytics and activity streams, social media. Technologists supporting education and research have been using these approaches in a range of ways, finding where they can help solve critical problems and meet unmet needs. Many of these explorations are investigative: they are about getting a grasp of how the technologies work, what the data looks like, where there are organisational or ethical issues that need to be addressed, and what the skills are that we need to develop in order to fully exploit these emerging opportunities.
The Learning Registry has been described by Dan Rehak as “Social Networking for Metadata” (about learning resources) . Imagine pushing RSS feeds into the cloud of all the urls of learning resources you can imagine, from museums, from educational content providers, from libraries. This is about web-scale big data. Imagine that cloud also pulling in data about where those urls have been shared, on facebook, twitter, blogs, mailing lists. If you’ve tried out services like topsy.com or bit.ly analytics you’ll know that finding out information about url shares is possible and potentially interesting. Now imagine being able to ask interrogate that data, to see meta trends, to provide a widget next to your content item that pulls down the conversation being had around it. That is the vision of the learning registry. Anyone who has been involved with sharing learning materials will recognise the scenario on the left below.
Learning Registry Use Case, Amber Thomas, JISC 2012, CC BY
The Learning Registry is about applying the technologies described above to the problem on the left, by making it possible to mine the network for useful context to guide users.
To explore this potential, the JISC/HEA OER Programme has been funding an experiment to run a Learning Registry “node” in the UK. The growth of openly licensed content and the political momentum to encourage the use of that content has been a spur to funding this experiment though it should be noted that the Learning Registry is not designed purely for open content.
See this useful overview for more detail of the project. It has been running on an open innovation model, sharing progress openly, and working with interested people. Headed up by the excellent Sarah Currier, with input from Lorna Campbell and Phil Barker from JISC CETIS, in my view it has been a very effective experiment.
Towards the end of the work, on 22nd October 2012, Mimas hosted an expert meeting of those people that have been working with the Learning Registry, services and projects, contributors and consumers, developers and decision makers. It was a very rich meeting, with participants exchanging details of the way they have used these approaches, and deep discussions on what we have found.
What follows is my analysis of some of the key issues we have uncovered in this experiment.
Networks and Nodes
The structure of the LR is a fairly flat hierarchy, it can expand infinitely to accommodate new nodes, and nodes can cluster. See the overview for a useful diagram.
What this structure means is that it can grow easily, and that it does not require a governance model with large overheads. The rules are the rules of the network rather than of a gate-keeping organisation. This is an attractive model where it is not clear who the business case lies with.
One of the ways of running a node is to use an Amazon Web Service instance. That seems a nice pure way of running a distributed network, however university procurement frameworks have still got to adjust to the pricing mechanisms of the cloud. Perhaps in that respect we’re not ready to exploit cloud-based network services quite yet.
However more generally I think we are seeing is a growth in the profile of services that are brokers and aggregators of web content. Not the hosts, or the presentation layers, but services in between, sometimes invisible. JISC has been supporting the development of these sorts of “middleware”, “machine services” from the early days: the terminology changes but the concept is not new to JISC. What does seem to be developing though (and this is my perception) is an appetite for these intermediary services, and the skills to integrate them. Perhaps there is a readiness for a Learning Registry-ish service now.
Another key architectural characteristic is a reliance on APIs. This enables developers to create services to meet particular needs. Rather than a centralised model that collects feature requests from users, it allows a layer of skilled developers to create services around the APIs. The APIs have to be powerful to enable this though, so getting that first layer of rich API functionality working is key. To that extent the central team has to be fast and responsive to keep up momentum.
However the extent to which the LR is actually a network so far is unclear. There are a handful of nodes, but not to the extent that we can be sure we are seeing any network effects. The lack of growth of nodes may be because the barrier to setting up a node is perceived to be high. It may be too early to tell. But for the purposes of the JLeRN experiment, my conclusion is that we have not seen the network effects that the LR promises.
Pushing the hardest problems out of sight?
It’s easy to fall into a trap of hoping that one technical system will meet everybody’s needs. The Learning Registry might not be THE answer, but there is something of value in the way that it provides some infrastructure to manage a very complex distributed problem.
However the question raised at the workshop by Sarah Currier in her introduction and again by David Kay in his closing reflections is: does it push some of the challenges out of scope, for someone else to solve? The challenges in question include:
- Resource description and keywords
- People identifiers
- Data versioning
To take one problem area: resource description for learning materials. It is very hard to agree on any mandatory metadata beyond Dublin Core. This is partly because of the diversity of resource types and formats: a learning material can be anything, from a photo to a whole website. Within resource types it is possible to have a deeper vocabulary, for example for content packaged resources that may have a nominal “time” or “level” attached. Likewise, different disciplinary areas not only have specialist vocabularies but also use content in different ways. It is technically possible to set useful mandatory metadata BUT in practice it is rarely complied with. When we are talking about a diversity of content providers with different motivations, the carrots and sticks are pretty complicated. So users want rich resource description metadata, to aid search and selection, but that is rarely supplied.
The Learning Registry solution is to be agnostic about metadata: it just sucks it all into the big data cloud. It does not mandate particular fields. What it does is offer developers a huge dataset to prod, to model, to shape, and to pull out in whichever way the users want it. Developers can do anything they want with the data AS LONG AS THE DATA EXISTS. If there is not enough data to play with, or not enough consistency between resources, then it is hard to create meaningful services over the data.
I said above that the Learning Registry “provides some infrastructure to manage a very complex distributed problem”. But on reflection does it manage that complexity? Or does it just make it manageable by pushing it out of scope? And if it doesn’t enable developers to build useful services for educators, is it successful?
These are a selection of the issues that the experiment is surfacing. There are certainly plenty of question marks about the effectiveness of this sort of approach. But I still feel sure that there are aspects of these technologies that we should be applying to meeting our needs in education and research. Certainly, this experiment has overlapped with work in JISC’s Activity Data programme, in our analytics work and in the area of cloud solutions. There is something interesting happening here, some glimpses of more elegant ways of share content, maybe even a step change.