What is learning analytics?
This page defines what is learning analytics, how it differs from academic analytics, plus the principles that support good learning analytics practice.
Definition of learning analytics
Learning analytics uses data about learners and their learning to provide actionable insights that enhance teaching and learning. The University of Melbourne uses learning analytics to understand student engagement, improve subject delivery, and support your learning outcomes.
More formally, learning analytics is defined as '...the collection, analysis, interpretation and communication of data about learners and their learning that provides theoretically relevant and actionable insights to enhance learning and teaching.' (Solar, 2025).
Definition of academic analytics
Academic analytics by contrast operates at an institutional level, functioning similarly to business intelligence. It uses data analysis to inform administrative and organisational decision-making across institutional, regional, and international contexts (Siemens and Long, 2011). This encompasses faculty-level work to develop student profiles, and the analytics and reporting that universities provide to regulatory bodies such as TEQSA.
How student feedback feeds into learning analytics
An important component of learning analytics is student feedback gathered through the University's Student Learning Surveys, specifically the end of subject surveys. These surveys capture student responses about the learning experience and are integral to understanding teaching effectiveness and learning outcomes. This feedback directly informs how the University can improve teaching and learning.
For details on the survey process and how reports are distributed across the University, please refer to the pages below.
The distinction between learning analytics and academic analytics
It's important to note that the Teaching and Learning Innovation learning analytics service focuses specifically on learning analytics, not academic analytics. Whilst academic analytics operates at an institutional level to inform administrative and organisational decision-making, learning analytics concentrates on the learning process itself - investigating interactions between learners, content, institutions, and educators to improve learning outcomes (Siemens and Long, 2011).
| Type of analytics | Level or object of analysis | Who benefits |
|---|---|---|
| Learning analytics | Subject level: social networks, conceptual development, discourse analysis, “intelligent curriculum” | Learners, faculty |
| Departmental: predictive modelling, patterns of success/failure | Learners, faculty | |
| Academic analytics | Institutional: learner profiles, performance of academics, knowledge flow | Administrators, funders, marketing |
| Regional (state/provincial): comparison between systems | Funders, administrators | |
| National and international | National governments, education authorities |
Table from 'Penetrating the Fog: Analytics in Learning and Education' (Siemens and Long, 2011) .
Teaching and Learning Innovation learning analytics principles
To guide how Teaching and Learning Innovation manages learning analytics data and its learning analytics service, the below principles guide our work practices and underpin our procedures.
- Ensuring we have consent to collect data
- The generation, dissemination and use of learning analytics is for the benefit of students and their learning outcomes, avoiding bias, (Non-maleficence and beneficence)
- For the enhancement of teaching design and practice
- Transparent and ethical use of data
- Ensuring anonymity where ever practical
- Ensuring appropriate access controls
- Automated decision-making and profiling
- Best practice data management principles applied
- Community expectations
- Complying with University polices and legislative requirements.
Our focus
Teaching and Learning Innovation supports learning analytics to benefit students and enhance teaching practice; our focus remains exclusively on improving the learning experience.
References
- Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–32.
- Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review.
- Society for Learning Analytics Research. (2025). What is learning analytics? SoLAR.
- Corrin, L., Kennedy, G., French, S., Buckingham Shum S., Kitto, K., Pardo, A., West, D., Mirriahi, N., & Colvin, C. (2019). The Ethics of Learning Analytics in Australian Higher Education. A Discussion Paper.
- Lang, C., Siemens, G., Wise, A. F., Gašević, D., & Merceron, A. (Eds.). (2022). Handbook of learning analytics (2nd ed.). Society for Learning Analytics Research (SoLAR).