OUseful.Info, the blog…

Assuming my projects haven’t been cut out at the final acceptance stage because I haven’t yet submitted a revised project plan,

Preamble
As OU courses are increasingly presented through the VLE, many of them opt to have one or more “Library Resources” pages that contain links to course related resources either hosted on the OU Library website or made available through a Library operated web service. Links to Library hosted or moderated resources may also appear inline in course content on the VLE. However, at the current time, it is difficult to get much idea about the extent to which any of these resources are ever accessed, or how students on a course make use of other Library resources.

With the state of the collection and reporting of activity data from the VLE still evolving, this project will explore the extent to which we can make use of data I do know exists, and to which I do have access, specifically Google Analytics data for the library.open.ac.uk domain.

The intention is to produce a three-way reporting framework using Google Analytics for visitors to the OU Library website and Library managed resources from the VLE. The reports will be targeted at: subject librarians who liaise with course teams; course teams; subscription managers.

Google Analytics (to which I have access) are already running on the library website and the matter just(?!) arises now of:

1) Identifying appropriate filters and segments to capture visits from different courses;

2) development of Google Analytics API wrapper calls to capture data by course or resource based segments and enable analysis, visualisation and reporting not supported within the Google Analytics environment.

3) Providing a meaningful reporting format for the three audience types. (note: we might also explore whether a view over the activity data may be appropriate for presenting back to students on a course.)

The Project
The OU Library has been running Google Analytics for several year, but to my knowledge has not started to exploit the data being collected as part of a reporting strategy on the usage of library resources resulting from referrals from the VLE. (Whenever a user clicks on a link in the VLE that leads to the Library website, the Google Analytics on the Library website can capture that fact.)

At the moment, we do not tend to work on optimising our online courses as websites so that they deliver the sorts of behaviour we want to encourage. If we were a web company, we would regularly analyse user behaviour on our course websites and modify them as a result.

This project represents the first step in a web analytics approach to understanding how our students access Library resources from the VLE: reporting. The project will then provide the basis for a follow on project that can look at how we can take insight from those reports and make them actionable, for example in the redesign of the way links to library resources are presented or used in the VLE, or how visitors from the VLE are handled when they hit the Library website.

The project complements work that has just started in the Library on a JISC funded project to making journal recommendations to students based on previous user actions.

The first outcome will be a set of Google Analytics filters and advanced segments tuned to the VLE visitor traffic and resource usage on the Library website. The second will be a set of Google analytics API wrappers that allow us to export this data and use it outside the Google Analytics environment.

The final deliverables are three report types in two possible flavours:

1) a report to subject librarians about the usage of library resources from visitors referred from the VLE for courses they look after

2) a report to librarians responsible for particular subscription databases showing how that resource is accessed by visitors referred from the VLE, broken down by course

3) a report to course teams showing how library resources linked to from the VLE for their course are used by visitors referred to those resources from the VLE.

The two flavours are:

a) Google analytics reports

b) custom dashboard with data accessed via the Google Analytics API

Recommendations will also be made based on the extent to which Library website usage by anonymous students on particular OU courses may be tracked by other means, such as affinity strings in the SAMS cookie, and the benefits that may accrue from this more comprehensive form of tracking.

If course team members on any OU courses presenting over the next 9 months are interested in how students are using the library website following a referral from the VLE, please get in touch. If academics on courses outside the OU would like to discuss the use of Google Analytics in an educational context, I’d love to hear from you too:-)

eSTEeM is joint initiative between the Open University’s Faculty of Science and Faculty of Maths, Computing and Technology to develop new approaches to teaching and learning both within existing and new programmes.

Here’s the presentation I gave to the judging panel at the TSO OpenUp competition final yesterday. As ever, it doesn’t make sense with[out] (doh!) me talking, though I did add some notes in to the Powerpoint deck: Opening up UCAS Course Code Data

(I had hoped Slideshare would be able to use the notes as a transcript, bit it doesn’t seem to do that, and I can’t see how to cut and paste the notes in by hand?:-(

A quick summary:

The “Big Idea” behind my entry to the TSO competition was a simple one – make UCAS course data (course code, title and institution) avaliable as data. By opening up the data we make it possible for third parties to construct services and applications based around complete data skeleton of all the courses offered for undergraduate entry through clearing in a particular year across UK higher education.
The data acts as scaffolding that can be used to develop consumer facing applications across HE (e.g. improved course choice applications) as well as support internal “vertical” activities within HEIs that may also be transferable across HEIs.
Primary value is generated from taking the course code scaffolding and annotating it with related data. Access to this dataset may be sold on in a B2B context via data platform services. Consumer facing applications with their own revenue streams may also be built on top of the data platform.
This idea makes data available that can potentially disrupt the currently discovery model for course choice and selection (but in its current form, not in university application or enrollment), in Higher Education in the UK.

Here are the notes I doodled to myself in preparation for the pitch. Now the idea has been picked up, it will need tightening up and may change significantly! ;-) Which is to say – in this form, it is just my original personal opinion on the idea, and all ‘facts’ need checking…

1. I thought the competition was as much about opening up the data as anything… So the original idea was simply that it would be really handy to have machine readable access to course code and course name information for UK HE courses from UCAS – which is presumably the closest thing we have to a national catalogue of higher education courses.

   But when selected to pitch the idea, it became clear that an application or two were also required, or at least some good business reasons for opening up this data…

   So here we go…

2. UCAS is the clearing house for applying to university in the UK. It maintains a comprehensive directory of HE courses available in the UK.

   Postgraduate students and Open University students do not go through UCAS. Other direct entry routes to higher education courses may also be available.

   According to UCAS, in 2010, there were 697,351 applicants with 487,329 acceptances, compared with 639,860 applications and 481,854 acceptances in 2009. [ Slightly different figures in end of cycle report 2009/10? ]

   For convenience, hold in mind the thought that course codes could be to course marketing, what postcodes are for geo related applications… They provide a natural identifier that other things can be associated with.

   Associated with each degree course is a course code. UCAS course codes are also associated with JACS codes – Joint Academic Coding System identifiers – that relate to particular topics of study. “The UCAS course codes have no meaning other than “this course is offered by this institution for this application cycle”.” link]

   “UCAS course code is 4 character reference which can be any combination of letters and numbers.

   Each course is also assigned up to three JACS (Joint Academic Coding System) codes in order to classify the course for *J purposes. The JACS system was introduced for 2002 entry, and replaced UCAS Standard Classification of Academic Subjects (SCAS). Each JACS code consists of a single letter followed by 3 numbers. JACS is divided into subject areas, with a related initial letter for each. JACS codes are allocated to courses for the *J return.

   The JACS system is used by the Higher Education Statistics Agency (HESA), and is the result of a joint UCAS-HESA subject code harmonization project.

   JACS is also used by UK institutions to identify the subject matter of programmes and modules. These institutions include the Department for Innovation, Universities and Skills (DIUS), the Home Office and the Higher Education Funding Council for England (HEFCE).”
   —
   Keywords: up to 10 keywords per course are allocated to each course from a restricted list of just over 4,500 valid keywords.
   “Main keyword: This is generally a broad subject category, usually expressed as a single word, for example ‘Business’.
   Suggested keyword (SUG): Where a search on a main keyword identifies more than 200 courses, the Course Search user is prompted to select from a set of secondary keywords or phrases. These are the more specific ‘Suggested keywords’ attached to the courses identified. For example, ‘Business Administration’ is one of a range of ‘Suggested keywords’ which could be attached to a Business course (there are more than 60 others to choose from). A course in Business Administration would typically have this as the ‘Suggested keyword’, with ‘Business’ as the main keyword.
   However, if a course only has a ‘Suggested keyword’ and not a related ‘Main keyword’, the course will not be displayed in any search under the ‘Main keyword’ alone.

   Single subject: Main keywords can be ticked as ‘Single subject’. This means that the course will be displayed by a keyword search on the subject, when the user chooses the ‘single subject’ option below. You may have a maximum of two keywords indicated as single subjects per course.”

   “Between January and March 2010, approximately 600,000 unique IP addresses access the UCAS course code search function. During the same time period, almost 5 million unique IP addresses accessed the UCAS subject search function.” [link]
   —
   “New courses from 2012 will be given UCAS codes that should not be used for subject classification purposes. However, all courses will still be assigned up to three individual JACS3 codes based on the subject content of the course.

   An analysis of unique IP address activity on the UCAS Course Search has shown that very few searches are conducted using the course code, compared to the subject search function. UCAS Courses Data Team will be working to improve the subject search and course keywords over the coming year to enable potential applicants to accurately find suitable courses.” [link]
   —

   Course code identifiers have an important role to play within a university administrations, for example in marshalling resources around a course, although they are not used by students. (On the other hand, students may have a familiarity with module codes.) Course codes identify courses that are the subject of quality assessment by the QAA. To a certain extent, a complete catalogue of course codes allows third parties to organise offerings based around UK higher education degrees in a comprehensive way and link in to the UCAS application procedure.

3. If released as open data, and particularly as Linked Open Data, the course data can be used to support:
   - the release of horizontal data across the UK HE sector by HEIs, such as course catalogue information;
   - vertical scaffolding within an institution for elaboration by module codes, which in turn may be associated with module descriptions, reading lists, educational resources, etc.
   - the development across HE of services supporting student choice – for example “compare the uni” type services
4. At the moment the data is siloed inside UCAS behind a search engine with unfriendly session based URLs and a poor results UI. Whilst it is possible to scrape or crowd-source course code information, such ad hoc collection mechanisms run the danger of being far from complete, which means that bias may be introduced into the collection as a side effect of the collection method.
5. Making the data available via an API or Linked data store makes it easier for third parties to build course related services of whatever flavour – course comparison sites, badging services, resource recommendation services. The availability of the data also makes it easier for developers within an intsitution to develop services around course codes that might be directly transferable to, or scaleable across, other institutions.
6. What happens if the API becomes writeable? An appropriately designed data store, and corresponding ingest routes, might encourage HEIs to start releasing the course data themselves in a more structured way.

   XCRI is JISC’s preferred way of doing this, and I think there has been some lobbying of HEFCE from various JISC projects, but I’m not sure how successful it’s been?

7. Ultimately, we might be able to aggregate data from locally maintained local data stores. Course marketing becomes a feature of the Linked Data cloud.

   Also context of data burden on HEIs, reporting to Professional, Statutory and Regulatory Bodies – PSURBS.

   Reconciliation with HESA Institution and campus identifiers, as well as the JISCMU API and Guardian Datablog Rosetta Stone spreadsheet

   By hosting course code data, and using it as scaffolding within a Linked Data cloud around HE courses, a valuable platform service can be made available to HEIs as well as commercial operations designed to support student choice when it comes to selecting an appropriate course and university.

8. Several recent JISC project have started to explore the release of course related activity data on the one hand, and Linked Data approaches to enterprise wide data management on the other. What is currently lacking is national data-centric view over all HEI course offerings. UCAS has that data.

   Opening up the data facilitates rapid innovation projects within HEIs, and makes it possible for innovators within an HEI to make progress on projects that span across course offerings even if they don’t have easy access to that data from their own institution.

9. Consumer services are also a possibility. As HEIs become more businesslike, treating students as customers, and paying customers at that, we might expect to see the appearance of university course comparison sites.

   CompareTheUni has had a holding page up for months – but will it ever launch? Uni&Books crowd sources module codes and associated reading links. Talis Aspire is a commercial reading list system that associates resources with module codes.

10. Last year, I pulled together a few separate published datasets and through them into Google Fusion Tables, then plotted the results. The idea was that you could chart research ratings against student satisfaction, or drop out rates against the academic pay. [link ]

    Guardian datablog picked up the post, and I still get traffic from there on a daily basis… [link ]

11. The JISC MOSAIC Library data challenge saw Huddersfield University open up book loans data associated with course codes – so you could map between courses and books, and vice versa (“People who studied this course borrowed this book”, “this book was referred to by students on this course”)

    One demonstrator I built used a bookmarklet to annotate UCAS course pages with a link to a resource page showing what books had been borrowed by students on that course at Huddersfiled University. [Link ]

12. Enter something like compare the uni, but data driven, and providing aggregated views over data from universities and courses.
13. To set the scene, the site needs to be designed with a user in mind. I see a 16-17 year old, sloughing on the sofa, TV on with the most partial of attention being paid to it, laptop or tablet to hand and the main focus of attention. Facebook chat and a browser are grabbing attention on screen, with occasional distractions from the TV and mobile phone.
14. The key is course data – this provides a natural set of identifiers that span the full range of clearing based HE course offerings in the UK and allows third parties to build servies on this basis.

    The course codes also provide hooks against which it may be possible to deploy mappings across skills frameworks, e.g. SFIA in IT world. The course codes will also have associated JACS subject code mappings and UCAS search terms, which in turn may provide weak links into other domains, such as the world of books using vocabularies such as the Library of Congress Subject headings and Dewey classification codes.

15. Further down the line, if we can start to associate module codes with course codes, we can start to develop services to support current students, or informal learners, by hooking in educational resources at the module level.
16. Marketing can go several ways. For the data platform, evangelism into the HE developer community may spark innovation from within HEIs, most likely under the auspices of JISC projects. Platform data services may also be marketed to third party developers and innovators/entrepeneurs.

    Marketing of services built on top of the data platform will need to be marketed to the target audience using appropriate channels. Specialist marketers such as Campus Group may be appropriate partners here.

17. The idea pitched is disruptive in that one of the major competitors is at first UCAS. However, if UCAS retains it’s unique role in university application and clearing, then UCAS will still play an essential, and heavily trafficked, role in undergraduate student applications to university. Course discovery and selection will, however, move away from the UCAS site towards services that better meet the needs of potential applicants. One then might imagine UCAS becoming a B2B service that acts as intermediary between student choice websites and universities, or even entertain a scenario in which UCAS is disintermediated and some other clearing mechanism instituted between universities and potential-student facing course choice portals.
18. According to UCAS, between January and March 2010 “almost 5 million unique IP addresses accessed the UCAS subject search function” [link] In each of the last couple of years, the annual application/acceptance numbers have been of the order approx 500,000 students intake per year, on 600,000 applicants. If 10% of applicants and generate £5 per applicant, that’s £300k pa. £10 from 20% of intake, that’s £1M pa. £50 each from 40% is £10M. I haven’t managed to find out what the acquisition cost of a successful applicant is, or the advertising budget allocated to an undergraduate recruitment marketing campaign, but there are 200 or so HE institutions (going by the number of allocated HESA institution codes).

    For platform business – e.g. business model based around selling queries on linked/aggregated/mapped datasets. If you imagine a query returning results with several attributes, each result is a row and each attribute is a column, If you allow free access to x thousand query cells returned a day, and then charge for cells above that limit, you:
    Encourage wider innovation around your platform; let people run narrow queries or broad queries. License on use of data for folk to use on their own datastores/augmented with their own triples.
    Generate revenue that scales on a metered basis according to usage;
    - offer additional analytics that get your tracking script in third party web pages, helping train your learning classifiers, which makes platform more valuable.

    For a consumer facing application – eg a course choice site for potential appications is the easiest to imagine:
    - Short term model would be advertising (e.g. course/uni ads), affiliate fees on booksales for first year books? Seond hand books market eg via Facebook marketplace?
    - Medium term – affiliate for for prospectus application/fulfilment
    Long term – affiliate fee for course registration

19. At the end of the day, if the data describing all the HE courses available in the UK is available as data, folk will be able to start to build interesting things around it…

Preamble
If the desire for OU courses to make increased use of third party materials and open educational resources is realised, we are likely to see a shift in the pedagogy to one that is more resource based. This project seeks to explore the extent to which custom search engines tuned to particular courses may be used to support the discovery of appropriate resources published on the public web, and as indexed by Google, on any given course.

Many courses now include links to third party resources that have been published on the public web. Discovering appropriate resources in terms of relevance and quality can be a time consuming affair. The Google Custom Search Engine service allows users to define custom search engines (CSEs) that search over a limited set of domains or web pages, rather than the whole web.

(Topic based links can be discovered in a wide variety of places. For example, it is possible to create custom search engines based around the homepages of people added to a Twitter list, or the nominated blogs in annual award listings.)

The ranking of particular resources may also be boosted in the definition of the CSE via a custom ranking configuration. For example, open educational resources published in support of the course may be boosted in the search result rankings.

Alternatively, CSEs may be used to exclude results from particular domains, or return resources from the whole web with the ranking of results from specified pages or domains boosted as required. By opening up results to the whole of the web, if recent, relevant resources from an unspecified domain are identified in response to a particular search query, they stand a chance of being presented to the user in the results listing.

Synonyms for common terms may also be explicitly declared and refinement labels used to offer facet based search limits. This might be used to limit results to resources identified as particularly relevant for a particular unit, or block within a course, for example, or to particular topic areas spread across a course.

“Promoted” results may also be used to emphasise particular results in response to particular queries. A good example here might be to display promoted results relating to resources explicitly referenced in an exercise, assignment or activity.

If any of the indexed pages are marked up with structured data, it may be possible to expose this data using an rich snippet/enhanced search listing. Whilst there are few examples to date, enhanced listings that display document types or media types might be appropriate.

Examples of Google CSEs in action can be found here:

- Digital Worlds Cusotm Search Engine (created by hand; as used in T151).

- faceted “HE CSE” metasearch engine over UK Higher Education Library websites, UK Parliamentary pages, OERs, video protocols for science experiments. This example demonstrates how the search engine may be embedded in a web page.

The Project
The project proposes the automated generation of custom search engines on a per course basis based on the resources linked to from any given course.

The deliverables will be:

1) an automated way of generating Google CSE definition files through link scraping of Structured Authoring/XML versions of online course materials. If necessary, additional scraping of non-SA, VLE published resources may be required.

2) a resource template page and/or widget in the VLE providing access to the customised course search engine

Success will be based on the extent to which:

1) students on pilot courses use the search engine;
2) a survey of students on courses using the search engine about how useful they found it

Search engine metrics will also form part of the reporting chain. If appropriate, we will also explore the extent to which search engine analytics can be used to enhance the performance of the search engine (for example, by tuning custom ranking configurations), as well offering “recent searches” information to students.

The placement of the search box for the CSE will be an important factor and any evaluation should take this into account, e.g. through A/B testing on course web pages.

Another variable relating to the extent to which a CSE is used by students is whether the CSE performs a whole web search with declared resources prioritised, or whether it just searches over declared resources. Again, an A/B test may be appropriate.

For activities that include a resource discovery component, it would be interesting to explore what effect embedding the search engine with the activity description page might have?

If course team members on any OU courses presenting over the next 9 months are interested in trying out a course based custom search engine, please get in touch. If academics on courses outside the OU would like to discuss the creation and use of course search engines for use on their own courses, I’d love to hear from you too:-)

eSTEeM is joint initiative between the Open University’s Faculty of Science and Faculty of Maths, Computing and Technology to develop new approaches to teaching and learning both within existing and new programmes.