Posts tagged data mining
When we think of reading lists we often try to look at it from the point of view of one or more of our user communities. We’ve got academics who want to have a quick and easy way to add and remove material from lists that they can then use to guide their students’ learning. We’ve got the students themselves who want pointers to works that they’ll need for reports and projects, preferably with as many online resources as possible. And we’ve got the librarians who need to use reading lists to help manage the book stocks and ensure that library budgets are spent wisely.
However today I’ve been hacking on some code for a different user community: prospective students. There’s actually really two camps here as well: people who are thinking about applying for a course at the University and those that have already applied and are waiting to be accepted and join their course in the next academic year. In either camp though there are individuals who would like to get guidance on the sort of material they will be expected to read when they come to the University to study. Our librarians already get the occasional request come in along these lines so we know there is a latent interest in this. And this is where the reading list data base can come in….
The LORLS database at Loughborough holds quite a lot of data on module reading lists., with a pretty high coverage rate of modules across all the schools and departments on campus. As part of academics providing information about each work on their list we’ve asked them to, where possible, make judgements as to the level of recommendation each work has. For example they may say that one book is absolutely essential, another comes highly recommended if you can get it and a third is OK as an optional reading source to provide a fuller understanding of a topic. Obviously only the really core texts for a module are marked as essential, even on really large reading lists.
My new program makes use of these academic supplied recommendations to find out what books are judged as essential on foundation and first year undergraduate modules within a department. Whilst not all of these modules in all departments will be compulsory for all students, they do give a good idea of what the core reading is likely to be for students in their initial year on campus. Indeed having a list of essential works to flick through in a local library or bookshop (or via extracts on Google Books!) before they turn up may help some prospective students make quicker decisions about any module options they are offered.
The code is pretty Loughborough centric so it isn’t likely to appear in the LORLS distributions directly: it has to wander over to our central information systems front end to get a list of modules for a department’s first year (as some departments may share modules on joint honours courses). In the future we might want to get more granularity by allowing prospective students to specify not only the department they are interested in but the programme of study itself (by name and/or UCAS code for example). However we get the list of modules though, the next step is purely based on running through the LORLS database to find the works that have essential recommendation levels at the moment.
Of course that list of works might be extended if this is actually implemented as a production service. For example if a department doesn’t have any works in any foundation or first year module marked as “essential” (some don’t!), we might want the code to fall back to looking at recommended works that are heavily borrowed (so linking in with our Aleph LMS’s borrower history as we’ve done in the past for our high demand reports and purchase prediction code). Or the University might want to add some non-academic pastoral support texts to the list (time and money management techniques, study skills, etc). Lots of options to consider there.
Once we’ve got the list of books that can be used as suggestions to the prospective students, we have some more options in how to present them. At the moment the code implements two options. The first of these is to display a simple HTML page containing the citations. Each work’s ISBN is hyperlinked to Google Books so that the prospective student (or their friends and family) can see cover art, read some extracts and potentially buy a copy online.
At the bottom of this list we also provide the second option: a link to allow the student to add the works found to a new virtual bookshelf on Goodreads.com. From there they can look at reviews, rankings, share with their friends, and do all the other social reading things that Goodreads permits. We called the new shelf “loughborough-wishlist” as it may well be the books they’d like to buy (or have others buy for them!).
This second option is a specific implementation of something we’ve been thinking about more generally for a while: getting our LORLS data linked in with various third party social media and sharing services that the users are already using. We picked Goodreads.com because the API is clean and provides exactly what we need. We’d like to do it with other services such as LibraryThing.com, Google Books, Amazon wishlists or even Facebook (which does have a Books API surprisingly).
We are now waiting for our library colleagues to evaluate the results before we consider going live with this system. We’ll probably want to pass it by the academic departments before it gets sent out to prospective students so that they have a chance to check what, if any, recommendations have been made for readings during the first year that will show up to prospective students.
However, what this program does demonstrate though is that once we have a suitably large body of data in a library system we can start to look at it in new ways and help new user communities. In this case the more that can be done to help engage prospective students and ease their transition into being a student, the better the chance that they’ll make the right choice and pick Loughborough as the place for their academic career.