Last week I went to the hackday organised by the JLeRN team and CETIS to kick off Mimas’ JLeRN Experiment. It you haven’t come across JLeRN before, it’s a JISC funded exploratory project to build an experimental Learning Registry node. The event, which was organised by JLeRN’s Sarah Currier and CETIS’ dear departed John Robertson, brought a small but enthusiastic bunch of developers together to discuss how they might use and interact with the JLeRN test node and the Learning Registry more generally.
One of the aims of the day was to attempt to scope some usecases for the JLeRN Experiment, while the technical developers were discussing the implementation of the node and exploring potential development projects. We didn’t exactly come up with usecases per se, but we did discuss a wide range of issues. JLeRN are limited in what they can do by the relatively short timescale of the project, so the list below represents issues we would like to see addressed in the longer term.
The Learning Registry (LR) could provide a valuable opportunity to gather accessibility stories. For example it could enable a partially-sighted user to find resources that had been used by other partially-sighted users. But accessibility information is complex, how could it be captured and fed into the LR? Is this really a user profiling issue? If so, what are the implications for data privacy? If you are recording usage data you need to notify users what you are doing.
Capturing, Inputting and Accessing Paradata
We need to consider how systems generate paradata, how that information can be captured and fed back to the LR. The Dynamic Learning Maps curricular mapping system generates huge amounts of data from each course; this could be a valuable source of paradata. Course blogs can also generate more subjective paradata.
A desktop widget or browser plugin with a simple interface, that captures information about users, resources, content, context of use, etc would be very useful. Users need simplified services to get data in and out of the LR.
Once systems can input paradata, what will they get back from the LR? We need to produce concrete usecases that demonstrate what users can do with the paradata they generate and input. And we need to start defining the structure of the paradata for various usecases.
There are good reasons why the concept of “actor” has been kept simple in the LR spec but we may need to have a closer look at the relationship between actors and paradata.
Setting Up and Running a Node
It’s difficult for developers to find the information they need in order to set up a node as it tends to be buried in the LR mailing lists. The relevant information isn’t easily accessible at present. The “20 minute” guides are simple to read but complex to implement. It’s also difficult to find the tools that already exist. Developers and users need simple tools and services and simplified APIs for brokerage services.
Is it likely that HE users will want to build their own nodes? What is the business model for running a node? Running a node is a cost. Institutions are unlikely to be able to capitalise on running a node, however they could capitalise by building services on top of the node. Nodes run as services are likely to be a more attractive option.
Suggestions for JISC
It would be very useful if JISC funded a series of simple tools to get data into and out of JLeRN. Something similar to the SWORD demonstrators would be helpful.
Fund a tool aimed at learning technologists and launch it at ALT-C for delegates to take back to their institutions and use.
A simple “accessibility like” button would be a good idea. This could possibly be a challenge for the forthcoming DevEd event.
Nodes essentially have to be sustainable services but the current funding model doesn’t allow for that. Funding tends to focus on innovation rather than sustainable services. Six months is not really long enough for JLeRN to show what can really be done. Three years would be better.
With thanks to…
Sarah Currier (MIMAS), Suzanne Hardy (University of Newcastle), Terry McAndrew (University of Leeds), Julian Tenney (University of Nottingham), Scott Wilson (University of Bolton).