Learner profiles: A more holistic view of learning - Education Matters Magazine
Expert Contributors, Opinion

Learner profiles: A more holistic view of learning

Learner profiles

Learner profiles focus on capturing capabilities such as critical thinking and collaboration, and providing a comprehensive view of an individual student’s learning profile, according to Dr Vitomir Kovanovic of the University of South Australia. 

There is a growing call for schools to move beyond graded assessment scores to include the skills and capabilities that students require to thrive in the future. One approach that has gathered significant interest is learner profiles.

In a recent paper, we reviewed several learner profile initiatives being implemented in Australia and internationally. It is important to understand the key benefits and challenges of these different forms of learner profiles and how they can best serve our schools and students.


Learner profiles are visual representations of students that paint a richer picture of their learning than grades and academic scores alone. They typically include information about a student’s learning progress, extracurricular activities, learning behaviours, self-regulation, and wellbeing.

Most learner profiles focus on capturing capabilities such as critical thinking and collaboration, on providing a more comprehensive view of student learning and, possibly, a future alternative to ATAR-based university admission.

Secondary student learner profiles
Student learner profiles include information about a student’s learning progress, extracurricular activities, learning behaviours, self-regulation, and wellbeing.


Firstly, learner profiles help teachers identify developmental needs and personalise student learning to address those needs. They provide a holistic view of students, which can help teachers understand student strengths and their progress across multiple subject areas.

Learner profiles also allow students to make better career choices and transition into the workplace or further studies. Instead of the one-size-fits- all approach provided by ATAR and grade point averages, learner profiles allow more tailored career pathways and better transition between different systems.

By focusing on a wide range of data, learner profiles provide richer insights into student learning, allowing teachers to better understand students. Such data can also be used to analyse student cohorts (an entire class or a year level).
By visualising historical data, learner profiles help identify trends and patterns that would otherwise go unnoticed.

Access to such data could improve student support and counselling by focusing the conversation with students on the actual data and evidence of their learning progress. Learner profiles can also help coordinate and share information between teachers, which is often time-consuming and manual.


The fundamental challenge is the lack of clarity about learner profiles and what kind of data they should contain. Each profiling approach uses different constructs and data, making profiles vague. This is particularly evident in how capabilities are defined, with a wide range of similar but still different terms.

It is also unclear whether each learning area should focus on developing a specific set of capabilities or whether each capability should be integrated into every learning area.

Another critical problem concerns data collection, as some of the most useful data, such as data on capabilities, is usually captured manually. This raises the issue of the quality of judgement and equal and fair treatment for all students since each teacher might have different interpretations of relevant constructs.

The manual data capture also brings significant workload challenges, as collecting such data requires substantial time and effort by the teachers. At the C3L research centre at UniSA, we are exploring how artificial intelligence and machine learning can help with data collection and reduce workload challenges.

Our work shows significant potential to automate large portions of data collection for some capabilities, such as critical and creative thinking. Such techniques would make it easier to adopt learner profiles and help reduce the already heavy teacher workload.

There is also an open question on integrating learner profiles into existing school practices. Any change to complex systems such as schools requires a staged approach and significant teacher input on what works and what doesn’t. There is a need for substantial professional development to help teachers integrate learner profiles into their teaching. This includes understanding learner profile dashboards and new data collection processes.

Finally, there is a need to shift from an assessment-oriented mindset to a development-oriented mindset. We also need to strive to assess learning progressions rather than performances at specific points in time. Such change requires a massive cultural shift and changes in incentives and constraints on school processes.

Contributing authors: Dr Vitomir Kovanovic,Dr Abhinava Barthakur, Professor Shane Dawson – Education Futures, University of South Australia, Adelaide SA.
Dr Vitomir Kovanovic, Senior Lecturer at the University of South Australia.


Dr Vitomir Kovanovic is a Senior Lecturer in Learning Analytics at the Centre for Change and Complexity in Learning (C3L) at the University of South Australia. His research focuses on helping schools use machine learning and artificial intelligence to support student learning and teacher decision making. Dr Kovanovic obtained his PhD in Informatics at the University of Edinburgh, the United Kingdom, in 2017. He is currently the Editor of the Journal of Learning Analytics (JLA) and an Associate Editor at the Higher Education Research & Development (HERD) Journal.



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