Learning analytics

Learning analytics is 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.

Historically, some of the most common uses of learning analytics is the prediction of student academic success and identification of at-risk students in order to provide them with support. Analytics is not just limited to focusing on students. Data drawn from student interactions and online engagement can be used to improve teaching practices and resources. Additional goals of learning analytics include to:

  1. support student agency and self-regulation
  2. provide personalised and timely feedback to students
  3. improve teaching practices through the use of empirical evidence on the success of pedagogical innovations
  4. develop both student and educator awareness by supporting self-reflection


There are four main methodologies for learning analytics: descriptive, diagnostic, predictive and prescriptive.

Strategies for applying these methodologies within the context of Monash are further explained within this resource, including instructions of how to access analytics within learning technologies to optimise learning.

Education Performance Standards Framework

The Education Performance Standards identify the expectations of education practice at Monash – See the Education Performance Standards for more details.

Impact on student learning

Impact on educational knowledge

Impact on educational environment

Effective teaching and learning

Responsive program design

Student- centred orientation

Professional learning engagement

Pedagogical content knowledge

Education research performance

Education innovation

Education leadership



You could address these Practice Elements by providing evidence of how you:

  • Improve teaching practices through the use of empirical evidence on the outcomes of pedagogical innovations
  • Adopt evidence-led rationale for unit/ course design
  • Provide targeted or personalised support to students through the use of data Empower students to reflect upon their learning through the use of learning analytics

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