Analytics at Sydney

An increasing focus on technology in education has seen the advent of learning analytics, and along with it the possibility of improving and personalising the learning experience of our students. Analytics is a key priority for the University moving forward, and we are engaging with analytics at Sydney as never before.

The Education Portfolio is playing a lead role in enabling learning analytics at the University, both at the curriculum and institutional levels. Much of this work is taking place via the new Quality and Analytics Group within the Education Portfolio, in partnership with the new Educational Innovation Team within the Education Portfolio, the Planning & Information Office, and ICT, as well as experts in several faculties including Engineering & IT and Education & Social Work.

In 2015 a Working Group of the SEG Education Committee developed 'Principles for the use of University-held student personal information for Learning Analytics at the University of Sydney'. These principles were endorsed by SEG Education in December 2015, and will be discussed by the SEG Committee at a March 2016 meeting. The finalisation of these principles will lead to a program of work throughout the insitution in 2016 to both enable learning analytics and to (where necessary) update our procedures to align with these principles.

The Sydney Learning Analytics Research Group (LARG) has been initiated to further enable research work on a variety of topics including learning analytics, educational data mining, and learner analytics and personalisation. It is a special interest group of the new Centre for Learning for Research on Learning and Innovation, and is open to all staff and students at the University of Sydney with an interest in learning analytics. Find out more and join the LARG email list here.

We are also working to make more analytics tools available to Sydney teachers, the first of which are the Track and Connect program, the Knowing Your Students report, four Blackboard Analytics reports, and the Student Relationship Engagement System software.

Staff at Sydney are contributing actively to various state, national, and international learning analytics groups. This includes: coordination of the activities of the NSW Learning Analytics Working Groupinstitutional membership of the Society for Learning Analytics Research (SoLAR); representation on the leadership teams of the following societies: SoLAR, the ascilite Learning Analytics SIG, the International Educational Data Mining Society; as well as editorial responsibilities on the two key journals, the Journal of Learning Analytics and the Journal of Educational Data Mining.

If you have any questions or comments regarding analytics at Sydney, please contact Kathryn Bartimote-Aufflick.

Stay connected

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Recent analytics events

  • In late November 2015, we hosted the Australian Learning Analytics Summer Institute (ALASI) at Sydney. Over 100 delegates joined us for this highly interactive two-day event for practitioners and researchers from universities across Australia (and beyond) for a program comprised mostly of workshops, tutorials and panels. The keynote speakers were Professor Peter Reimann from Sydney and Dr Lyn Alderman from QUT - resources from their talks are available here
  • On 30 June 2015, the University of Sydney hosted the monthly meeting of the NSW Learning Analytics Working Group – a network of academic and professional staff across many of the universities in the Sydney metropolitan region. The program consisted of lunch followed by two talks and then a plenary discussion:
    • ‘Using academic data to personalise support in a large faculty’, Professor Adam Bridgeman
    • ‘A multimodal method for analysing complex learning’, Dr Kate Thompson
    • ‘Academic analytics versus learning analytics – Rivals or complementary strangers?’, Plenary discussion
    • Videos and slides
  • On 27 July 2015, Professor Gregor Kennedy from the University of Melbourne gave a talk entitled ‘Logging students: Understanding digital learning one click at a time’.