What is Learning Analytics?

 Learning analytics begins with… “learning”

As defined by the University of Adelaide, “Learning Analytics” is:

“The practice of developing actionable insights through the collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.”

So, what value can learning analytics bring to stakeholders?

There are so many ways in which this data can be useful, some of these include:

  • Understanding student behaviour
  • Analysing problems and predicting outcomes
  • Collecting information for enhancement and data-driven decision making
  • Developing models and tools to support students and teachers
  • Informing curriculum development


Understanding learning analytics insights

Learning analytics can help piece together a story about success or risk in teaching and learning. For learning analytics to be valuable, it’s important for teaching staff to contribute context relevant to the designed activities and assessment approaches. There are several stages in the student lifecycle where data can be extracted to help improve the student academic experience and outcomes. By understanding student behaviour, we can develop insights to help contextualise the student experience and predict outcomes.


How can learning analytics insights inform learning design?

Insights gained from learning analytics can help shape the way a course is taught. The data used for developing these insights may be dynamic (as the course progresses) and/or historical (from courses already delivered), depending on the question being asked or the insight being sought. By understanding historical student behaviour, predictions can be made about the actions a student may take throughout a course. With this knowledge, courses can be catered to the needs of students in order to create the best possible learning outcomes.

The table below lists out some examples of questions that are normally asked from learning analytics at different stages of course delivery and the data that can be analysed:


Stage Data Examples
Before semester:

  • Who are my students?
  • What programs/careers are they enrolled in?


  • Demographics
  • High school achievement
  • Previous study or experiences
  • Previous test scores
  • Enrolment data


During semester:

  • How are my students going?
  • What are they doing?
  • What are they understanding?
  • Are they having problems in specific areas?
  • What is their level of engagement?



  • Class/tutorial attendance (if data is available)
  • First semester grades
  • Completion of pre-requisite courses
  • Grades in select core courses
  • Engagement with Teaching and Learning tools and systems
  • Progressive/midsemester assessment grades
  • Course enrolments for next semester
  • Use of library system
  • Campus Wi-Fi data
  • Historical course and Program data


After semester:

  • What did my students do?
  • What did they learn?
  • How does the content align to the program objectives?
  • Are there any trouble spots in the course design or technology used?
  • What improvements can be made?
  • What did the students think?
  • Course academic achievement results
  • Course and Program enrolment/engagement data
  • Program progression data
  • Data to support assessment review
  • Student success profiling (risk management)
  • Course and Program review and enhancement data
  • SELT data
  •  High Failure Rate reports
Teaching course again:

  • Re-design aspects of a course


  • Any data that can support informed decision making
  • Informed best practice
  • Data identifying successful courses or student outcomes

What specifically are we able to do with learning analytics at the University of Adelaide?

The Learning Analytics team within the Learning Enhancement and Innovation unit can assist with the following:

  • Data and visualisations relating to programs, courses, assessments and students
  • Building data prep workflows for automation of reports
  • Providing insights on how students learn
  • Information and support on how to use data to improve courses and programs
  • Generating ad-hoc reports including PeopleSoft, Cognos BI, MyUni etc.
  • Exploring learning theory, tools, applications and provide advice on best practice.


To find out more about how the Learning Analytics team can help you, email:

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