Are the developments in the HEFCE and JISC XCRI XML standards finally giving students the information they need to compare one university against the other? By standardising the output of content about courses, results and post graduation success in the job market, are students better positioned to evaluate the right University for them. This presentation will answer this question and discuss how universities can utilize Web Content Management to deliver on the demands of these standards and maximise student engagement.
2. About TERMINALFOUR
• Established in 1996
• Focus on Higher Education
• 101 education clients
• UCISA, UKOLN, Educause,
HiEdWeb, EduWeb, Edustyle,
Uwebd and AIIM Members
• Slough, Dublin,
Sydney & Boston
3. Focus for today…
• What is Open Data (and why is a hot topic)?
• How does it apply (or at least the concepts) to
Higher Education? (Jisc XCRI – HESA)
• The challenges
• Practical applications
• The benefits (for Students and Universities)
• Technical architecture: Using TERMINALFOUR to
link Banner (& other data sources)
4. What is Open Data / Linked Data?
• Current web: unstructured data
• Build your own interactive apps…
• Open Data: The sharing of structured
data between organisations
• Allows third parties to report upon or
build applications based on your data
(combined within third party data).
• Other benefits:
– Sector wide or international
comparisons
– Relatively small amount of work
required by you
5. Some fantastic resources / useful links on Open Data:
• Data.gov.uk
(UK Government Data)
• Data.open.ac.uk
(Open University Open Data Project)
• opendatachallenge.org
(Competition showing examples of what
can be done)
• openup.tso.co.uk/contest/
(TSO Open data competition)
• http://www.ted.com/talks/tim_berners_lee
_on_the_next_web.html
6. Why is this important to Universities…
HEFCE & Higher Education Statistics
Agency KIS Data Sets:
– KIS Data Sets
– HEFCE: NSS for course satisfaction
(including the new question on
students' union)
– HESA: DLHE for employability
– institutions: course information,
accommodation
– to be confirmed: fees and/or bursaries
and scholarships
– JISC XCRI Course Standard
7. Other uses…
• Managing your paper publishing process
– Prospectus
– Course Catalogues / Calendars
• Third party aggregation sites
• Experts / research databases
– Combining HR
– Research management Systems
– Citations Databases
– Student’s Record Systems
8. The big benefits…
• Help student compare courses
(excellent course search)
• Search Engine Optimisation Benefits
• Student recruitment (via white labeling)
• Reuse & repurpose your existing data
• Improve data quality
• Allow others to build apps based on your
data (and others)
• But… demands for this type of data are
increasing so… a need to automate
9. For example…
• Let’s consider:
“I’d like to compare the cost of student
accommodation across every University
in the UK”
• Tasks required:
– Find local accommodation costs
– Find the data for every other
University
– Combine data
– Link to a mapping system
– Build a reporting engine
10. A live example…
• Let’s consider:
“I’d like to compare the
cost of student
accommodation across
every University
in the UK”
Interesting articles:
• http://devcsi.ukoln.ac.uk/2011/08/22/open-
data-hackday/
• http://benosteen.wordpress.com/2011/07/26/
student-property-heatmap/
12. The challenge…
Data / technical:
• Identifying the data
• Combining data sources
• Workflow
• Quality Control
• Additional “soft” content
• Development (avoiding “a series of
technical scripts”)
Organisational:
• Resources / time pressures
• Content updates
13. We’ve built a solution to help…
• Key challenges…
– Easy integrate data from different
locations including Banner (securely)
– Combine it with “soft content”
– Identify and manage “changes”
– Publish to different formats and
channels
– Synchronise delivery to XML output
15. Conclusion
• Demands are increasing to publish,
export and share your data
• Strong need for an automated way of
handing & controlling the data (including
new data)
• Numerous data sources
• Data quality a key project requirement
Key challenges:
• Common keys between data
• Audit trails and version control (what
data set has been distributed to who)