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Introduction to learning analytics in your OLE

Note: This article is part of the Learning analytics collection.

The Society for Learning Analytics Research (SoLAR) defines learning analytics as

"the measurement, 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."
- Society for Learning Analytics Research (SoLAR) (2021)

Engaging with learning analytics promotes a data-driven, evidence-based approach to designing and delivering learning and teaching experiences, and can be employed at different levels (Buckingham Shum, 2012) ranging from:

  • cross-institutional reporting and benchmarking (macro-level)
  • institution-wide analytics used to inform management and resourcing (meso-level)
  • analytics that are delivered directly to teachers and students in the form of reports, tools or dashboards (micro-level)
Note: It is important to remember that learning analytics data sources measure student interactions with a subject site’s activities and resources. Interactions do not necessarily equate to student cognitive engagement and comprehension. Data on interactions with activities and resources cannot give a complete picture of an individual student’s learning. Personal circumstances, technological factors, and the pedagogical context need to be taken into account when interpreting the data and deciding the actions to take based on insights generated through learning analytics.


Why is learning analytics data useful?

Learning analytics data can be a powerful tool in supporting quality teaching and learning experiences, and there are benefits for both teachers and students, as data:

  • Can supplement insight from face-to-face student-teacher interactions
  • Provides at a glance cohort-level overviews of student progress through subject content as well as at the individual level
  • Creates opportunities for students to self-measure and track their progress against recommended goals (e.g. activity completion/progress bar)
  • Supports early intervention for students who appear to be struggling or not engaging with learning tasks

 

Ethical considerations

There are important ethical considerations with regards to the use of learning analytics data. These include:

  • the privacy of student data
  • transparency of use, so that students are clear on what is being collected and why
  • who has access to the data

UOW has included a statement regarding the collection of learning analytics within Subject Outline templates to ensure transparency with students:

“Where Learning Analytics data (such as student engagement with Moodle, access to recorded lectures, University Library usage, task marks, and use of SOLS) is available to the Subject Coordinator, this may be used to assist in analysing student engagement and to identify and recommend support to students who may be at risk of failure. If you have questions about the kinds of data the University uses, how we collect it, and how we protect your privacy in the use of this data, please refer to the Privacy page on the UOW website."

Data relating to student interactions in the subject Moodle site should not be shared beyond the teaching team and must be used in a sensitive and ethical manner during communications with students.

References

Buckingham Shum, S. (2012). Policy Brief: Learning analytics. UNESCO Institute for Information Technologies in Education. http://iite.unesco.org/files/policy_briefs/pdf/en/learning_analytics.pdf

Society for Learning Analytics Research (2021). What is Learning Analytics? SoLAR website.
https://www.solaresearch.org/about/what-is-learning-analytics/

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