Stellar Project blog in the Internet Archive

The Stellar project blog, including the final report can be found in the Internet Archive Wayback Machine at

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STELLAR final report

We’ve now published the Jisc Final report for the STELLAR project on this blog.  The report, all 80 pages of it, is now listed the Reports and Dissemination page as well as available from STELLAR Jisc Final Report.


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STELLAR project finishes

Today is the last day of the eighteen month STELLAR project, we’ve had our last Steering Group meeting, been to a Jisc programme meeting during July and produced a fairly hefty final report which is with Jisc and we plan to publish shortly through this blog and elsewhere.

It’s been a really interesting project for the Project team and we’ve learnt an enormous amount about what it takes to establish a linked data environment for a digital library of learning materials.  Many of our experiences are documented on this blog, in the documents in the Reports and dissemination section and will be in the final report.

Important lessons learnt for us are particularly around the very significant effort needed around metadata, particularly in an environment like ours where there are several different content types that need to be handled.  We’ve also found areas where applying linked data tools within the digital library are going to start to pay dividends for us in our future development plans for that system.  Being able to easily (and automatically) link to related material openly available in iTunesU is really useful.

I’d like to say thanks to the project team who have been working on STELLAR: particularly Liz Mallett and Sarah Brown who took on the Project Management, to James Alexander who led the technical development, to Lara Whitelaw and Alex Addyman for their metadata expertise, to Ruth Cammies and colleagues in the OU Archive and cataloguing teams, to Tom Welch for his work on the surveying and to Mathieu d’Aquin from KMi for his work on Stellar and particularly the adaptation of DiscOU.  Thanks also to colleagues on our Steering Group chaired by Gill Needham and others who have helped from across the library, and also to colleagues in the University who helped with the surveying, took part in focus groups or completed the surveys.  Last but not least thanks to Jisc and their programme manager Neil Grindley for their support in funding this project.

We’ve a few plans underway to disseminate what we’ve learnt and to take forward the recommendations that the project has made and we’ll be crystallising those over the next few months.

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STELLAR search tool screencast

STELLAR smenatic web proof of concept screenshotThe STELLAR search tool is only available to OU staff as the content that it is searching is not completely rights-cleared for public access.  So the project has put together a very short (around 1 minute) screencast of the STELLAR search tool in use so you can see how it works in practice.  You can download the Mp4 version of the video from the link below.  Filesize 1.6mb.

STELLAR DiscOU screencast

The screencast shows a sample search in the DiscOU Stellar tool, showing how the results appear and then goes through the options that the tool gives to refine your search through a series of concept filters.  The screencast goes on to show how the content from the OU Digital Library is shown and accessed through DiscOU.

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STELLAR May update

We’re now well into our Post-enhancement and evaluation stage as the project starts to draw to a close and this post will provide an update on the work over the last few weeks.

The STELLAR proof-of-concept tool
The project has created a semantic web tool to search the linked data representations of the digital library.   The tool has been created by Dr Mathieu d’Aquin from the Knowledge Media Institute and builds on previous work including the linked data materials created for the Jisc-funded LucSTELLAR smenatic web proof of concept screenshotero project that established, and a tool called DiscOU.

Essentially the tool (shown above) allows you to paste in (or type in) either a whole section of text or just a few search terms into a search box.  Ideally you would put in a rich piece of text rather than just a few simple search terms.   The text is then passed through a semantic meaning engine and concepts are matched against the concepts contained within the digital library dataset.  A selection of the closest matches are then displayed, along with a thumbnail image of the item.  These link through to the object in the Fedora digital library.

The tool lets the user revise their query by using a set of ‘sliders’ on the left of the screen to choose which concepts most closely match what they want.  STELLAR concept featureSo it’s possible to decide that you don’t want to see so much material that talks about ‘Ice’ or ‘Snow’ for example, but you do want to see more material on ‘Atmosphere’.  Changing the sliders and clicking on Discover gives you a new selection of material to choose from.

The difference with a semantic web tool is that you aren’t just matching a string of text, but are analysing the meaning of those words and finding related material.  So you could put in a search for a particular thing, say Mt St Helens, and the semantic search system will recognise that material about other volcanoes might also be relevant.

As well as retrieving material from the digital library the tool can also show related material from other datasets in, currently this includes material from OpenLearn.

STELLAR item displayEach item from the digital library is shown in a separate block, with the title of the item, a description of what it is and which course it comes from, alongside a thumbnail of the item itself.  For the proof-of-concept we just picked a selection of material from three OU modules (S100, D251 and L550) to include in the system to show how the system works, but items are indexed at a page level so you can get directly to the appropriate page.  Clicking on the title or thumbnail takes you directly to that item in the digital library and opens that up in a separate tab. Clicking on the course code takes you to a page showing the extensive metadata about that course or element of the course (e.g.  Finally the tool allows you to deselect items if they aren’t relevant to you.

Post-enhancement and evaluation stage
Current activity is mainly focusing on a series of evaluation activities such as focus groups to talk to stakeholders who were originally involved in the pre-enhancement stage.  From that survey we had a pool of some 170 people who were happy to be contacted again about STELLAR and we’ve drawn on that group for a series of workshops.  The workshops give them a chance to use the tool and comment on how they find it, how they might use it and what’s its value would be.  This will be followed up with some surveying.

Next steps
As we reach the last stage of the project we’ve also been starting to work on the final report and putting together our thoughts about the sustainability of semantic web technology in our digital library.

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Taking stock…

Throughout 2012 STELLAR has prepared the groundwork for the development work we are now undertaking. The pre-enhancement survey was conducted and the findings from this identified the value placed on legacy learning materials as well as informing the selection of the modules we have chosen to enhance. As we come the end of 2012 that enhancement is now under way. Currently the learning materials for the selected modules are being prepared for transformation into linked data and work is taking place to identify appropriate tools and ontologies.

Looking ahead, the new year promises to be a busy time for STELLAR. Early in 2013, a proof of concept tool will be developed which will highlight the enhancements made using linked data and allow us to demonstrate the improvements to stakeholders. We are in discussions with the OU’s Knowledge Media Institute (KMi) regarding work that they have previously done on a tool called DiscOU. This is currently being used as a browser plug-in which works with BBC programme pages.  It semantically analyses the content of the page and on that basis retrieves relevant pieces of OU content.  STELLAR will be working to adapt the semantic engine of DiscOU to build a similar ‘discovery’ tool for the learning materials, enabling university staff responsible for writing or amending module materials to locate old content they can refer to or reuse.

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