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

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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

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Contents
1. CSU Context
2. Principles in Learning Analytics
3. Learning Analytics in Higher Education
4. Lessons Learned
5. Future Developments

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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

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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

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Questions...

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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

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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

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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”

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• 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

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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

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Questions...

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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

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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)

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Questions...

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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

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Questions...

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5. Future Developments
Ars Electronica (CC BY-NC-ND)

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Questions...

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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

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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