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2014 02 learning analytics in an era of digitisation

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Learning analytics in an era of digitisation

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2014 02 learning analytics in an era of digitisation

  1. 1. Learning Analytics in an Era of Digitisation February 2014 Simon Welsh Assoc Professor Philip Uys Senior Learning Analytics Officer, Strategic Learning and Teaching Innovation Charles Sturt University Director, Strategic Learning and Teaching Innovation Charles Sturt University siwelsh@csu.edu.au puys@csu.edu.au DIVISION OF STUDENT LEARNING
  2. 2. Contents 1. CSU Context 2. Principles in Learning Analytics 3. Learning Analytics in Higher Education 4. Lessons Learned 5. Future Developments DIVISION OF STUDENT LEARNING
  3. 3. 1. CSU Context • Charles Sturt University is a regional and international, multi-campus institution with around 40,000 students • Approximately 60% of students undertake distance education courses, with a further 15% enrolled in blended courses • CSU has invested heavily in educational technologies to provide reliable and equitable access to resources for students and staff alike • In 2013, we developed a Learning Analytics Strategy which is now moving to implementation DIVISION OF STUDENT LEARNING
  4. 4. 1. CSU Context • Some of our educational technologies, include... Course Eval Yammer PebblePad Turnitin EASTS InPlace Blackboard Learn Digital Object Management System Subject forums and wikis PODs Adobe Captivate mLearn Bridgit Adobe Connect CSU Replay Simulations SMART Tools DIVISION OF STUDENT LEARNING
  5. 5. Questions... DIVISION OF STUDENT LEARNING
  6. 6. 2. Principles in Learning Analytics • • Learning Analytics is defined as: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs (SOLAR) Learning Analytics is about helping students succeed by providing: o students with the self-awareness and insight to optimise their learning behaviours; o teaching and support staff with insight to make meaningful adaptations to their practice, as well as effective interventions; and o evidence to enable the adaptation of learning and teaching systems DIVISION OF STUDENT LEARNING
  7. 7. 2. Principles in Learning Analytics Drivers University Course Subject Metrics and Methods Presentation Formats Affordances of LA Technologies Audiences Adaptations -Design - Behaviour - Systems DIVISION OF STUDENT LEARNING
  8. 8. 2. Principles in Learning Analytics • Learning Analytics is sometimes referred to as “big data” in an educational context – but there is a danger in this short-hand • Learning Analytics must be proximal to learning theory/science and design • Theory, pedagogy and University objectives on different levels help us understand what to measure, why and how to respond • Learning Analytics that is not connected to theory, pedagogy and outcomes is just “counting clicks” DIVISION OF STUDENT LEARNING
  9. 9. 2. Principles in Learning Analytics • Student success is a product of the interplay of the student, the teaching and the institution Student Engagement University Strategy and Policy Student Success Support Faculty Academic Support Student Services Learning and Teaching Design and Delivery DIVISION OF STUDENT LEARNING
  10. 10. 2. Principles in Learning Analytics • Learning Analytics requires trust to work • It is essential to have a strong Ethics and Privacy Framework in place • A key principle: that data is only used for the purpose for which it was originally gathered • The legal aspects may actually be the most straightforward – earning the trust of students and staff may be the real challenge • Theory and pedagogy gives focus and purpose DIVISION OF STUDENT LEARNING
  11. 11. Questions... DIVISION OF STUDENT LEARNING
  12. 12. 3. Learning Analytics in Higher Education • Increasing usage of educational technologies such as LMSs, etc (as described before) and wider usage in Universities in this era of digitisation • Learning Analytics is a rapidly growing field in higher education in Australia and around the world: “the data tsunami” (Simon Buckingham-Shum) • This growth is driven by a number of strategic issues affecting Universities – such as increasing enrolments, higher student expectations, lower funding • Learning Analytics becomes the new competitive advantage DIVISION OF STUDENT LEARNING
  13. 13. 3. Learning Analytics in Higher Education • With increasing interest in the field and the release of easy-touse analytic packages, Learning Analytics has been fragmented in many institutions • While the embrace of Learning Analytics should be encouraged, the opportunity is to move beyond the (often) simplistic analytics in many pre-built packages to develop analytics that reflect our institutions, our students and our teaching • This means moving to a multi-dimensional landscape, where we source and integrate data about a student from a wide variety of sources (often outside the LMS) DIVISION OF STUDENT LEARNING
  14. 14. Questions... DIVISION OF STUDENT LEARNING
  15. 15. 4. Lessons Learned • Learning Analytics is not just a technical challenge – it’s about people, culture and practice • For Learning Analytics to truly be an “adaptation engine”, strong stakeholder engagement (and trust) is essential • Critical to think through roles and responsibilities: Learning Analytics can’t just be about creating more work for academics/teaching staff • It is also an evolutionary process in its own right – Learning Analytics doesn’t just help others adapt, it must be adaptive in itself • LA required university-wide collaboration and integration DIVISION OF STUDENT LEARNING
  16. 16. Questions... DIVISION OF STUDENT LEARNING
  17. 17. 5. Future Developments Ars Electronica (CC BY-NC-ND) DIVISION OF STUDENT LEARNING
  18. 18. Questions... DIVISION OF STUDENT LEARNING
  19. 19. Summary • Learning Analytics is about prompting and informing adaptation • Analytics are required on different levels including course, subject and university • To do so requires our analytics to be proximal to university objectives, learning and teaching theory and design • Learning Analytics operates on trust • Learning Analytics works best where it is multi-dimensional • To achieve this, broad stakeholder engagement is required • The future is about inferring and influencing the occurrence of learning at an individual-level both online and off-line DIVISION OF STUDENT LEARNING
  20. 20. Thank You Simon Welsh Assoc Professor Philip Uys Senior Learning Analytics Officer, Strategic Learning and Teaching Innovation Charles Sturt University Director, Strategic Learning and Teaching Innovation Charles Sturt University puys@csu.edu.au siwelsh@csu.edu.au DIVISION OF STUDENT LEARNING

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