DIVISION OF STUDENT LEARNING
Learning Analytics in an Era of
Digitisation
February 2014
Simon Welsh
Senior Learning Analyt...
DIVISION OF STUDENT LEARNING
Contents
1. CSU Context
2. Principles in Learning Analytics
3. Learning Analytics in Higher E...
DIVISION OF STUDENT LEARNING
1. CSU Context
• Charles Sturt University is a regional and international,
multi-campus insti...
DIVISION OF STUDENT LEARNING
1. CSU Context
• Some of our educational technologies, include...
Course Eval
CSU Replay SMAR...
DIVISION OF STUDENT LEARNING
Questions...
DIVISION OF STUDENT LEARNING
2. Principles in Learning Analytics
• Learning Analytics is defined as:
the measurement, coll...
DIVISION OF STUDENT LEARNING
2. Principles in Learning Analytics
A Model of Learning Analytics in Higher Education
Metrics...
DIVISION OF STUDENT LEARNING
2. Principles in Learning Analytics
• Learning Analytics is sometimes referred to as “big dat...
DIVISION OF STUDENT LEARNING
• Student success is a product of the interplay of the student, the
teaching and the institut...
DIVISION OF STUDENT LEARNING
2. Principles in Learning Analytics
• Learning Analytics requires trust to work
• It is essen...
DIVISION OF STUDENT LEARNING
Questions...
DIVISION OF STUDENT LEARNING
3. Learning Analytics in Higher Education
• Increasing usage of educational technologies such...
DIVISION OF STUDENT LEARNING
3. Learning Analytics in Higher Education
• With increasing interest in the field and the rel...
DIVISION OF STUDENT LEARNING
Questions...
DIVISION OF STUDENT LEARNING
4. Lessons Learned
• Learning Analytics is not just a technical challenge – it’s about
people...
DIVISION OF STUDENT LEARNING
Questions...
DIVISION OF STUDENT LEARNING
5. Future Developments
Ars Electronica (CC BY-NC-ND)
DIVISION OF STUDENT LEARNING
Questions...
DIVISION OF STUDENT LEARNING
Summary
• Learning Analytics is about prompting and informing
adaptation
• Analytics are requ...
DIVISION OF STUDENT LEARNING
Thank You
Simon Welsh
Senior Learning Analytics Officer,
Strategic Learning and Teaching Inno...
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Learning Analytics presentation for Australian e-learning Congress Feb 14

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Learning Analytics in a Era of Digitisation - exploring the challenges and opportunities created by learning analytics

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Learning Analytics presentation for Australian e-learning Congress Feb 14

  1. 1. DIVISION OF STUDENT LEARNING Learning Analytics in an Era of Digitisation February 2014 Simon Welsh Senior Learning Analytics Officer, Strategic Learning and Teaching Innovation Charles Sturt University siwelsh@csu.edu.au Assoc Professor Philip Uys Director, Strategic Learning and Teaching Innovation Charles Sturt University puys@csu.edu.au
  2. 2. DIVISION OF STUDENT LEARNING Contents 1. CSU Context 2. Principles in Learning Analytics 3. Learning Analytics in Higher Education 4. Lessons Learned 5. Future Developments
  3. 3. DIVISION OF STUDENT LEARNING 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
  4. 4. DIVISION OF STUDENT LEARNING 1. CSU Context • Some of our educational technologies, include... Course Eval CSU Replay SMART Tools TurnitinPebblePad Digital Object Management System InPlace Adobe Captivate Bridgit Adobe Connect Blackboard Learn EASTS Yammer Simulations Subject forums and wikis PODs mLearn
  5. 5. DIVISION OF STUDENT LEARNING Questions...
  6. 6. DIVISION OF STUDENT LEARNING 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
  7. 7. DIVISION OF STUDENT LEARNING 2. Principles in Learning Analytics A Model of Learning Analytics in Higher Education Metrics and Methods Audiences Drivers Affordances of LA Technologies University Course Subject Adaptations -Design - Behaviour - Systems Governance and Management Presentation Formats Evaluation Student Success
  8. 8. DIVISION OF STUDENT LEARNING 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”
  9. 9. DIVISION OF STUDENT LEARNING • Student success is a product of the interplay of the student, the teaching and the institution 2. Principles in Learning Analytics Student Success Student Engagement Learning and Teaching Design and Delivery Support Faculty Academic Support Student Services University Strategy and Policy
  10. 10. DIVISION OF STUDENT LEARNING 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
  11. 11. DIVISION OF STUDENT LEARNING Questions...
  12. 12. DIVISION OF STUDENT LEARNING 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
  13. 13. DIVISION OF STUDENT LEARNING 3. Learning Analytics in Higher Education • With increasing interest in the field and the release of easy-to- use 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)
  14. 14. DIVISION OF STUDENT LEARNING Questions...
  15. 15. DIVISION OF STUDENT LEARNING 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
  16. 16. DIVISION OF STUDENT LEARNING Questions...
  17. 17. DIVISION OF STUDENT LEARNING 5. Future Developments Ars Electronica (CC BY-NC-ND)
  18. 18. DIVISION OF STUDENT LEARNING Questions...
  19. 19. DIVISION OF STUDENT LEARNING 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
  20. 20. DIVISION OF STUDENT LEARNING Thank You Simon Welsh Senior Learning Analytics Officer, Strategic Learning and Teaching Innovation Charles Sturt University siwelsh@csu.edu.au Assoc Professor Philip Uys Director, Strategic Learning and Teaching Innovation Charles Sturt University puys@csu.edu.au

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