Data in Schools Melbourne 2026

What can learning data tracked across a million assessments reveal about how children come to understand mathematics and misunderstand it? This session draws on longitudinal evidence from primary classrooms across Australia to explore the persistent misconceptions students carry, where they cluster within curriculum content threads, and the precise points at which understanding most often breaks down.


The same evidence is put to work at three levels: strategically, to inform school improvement planning; at the classroom level, to sharpen Tier 1 instruction; and at the individual level, to surface the prerequisite gaps that hold students back.


Attendees will gain insight into how this kind of evidence can be put to work at every level of a school and what becomes possible when longitudinal data is built into everyday assessment practice.

In this session, we will discuss some ethical considerations when it comes to using and operationalising student data in schools. We will look at the role of data in creating cultures of surveillance, increased teacher workload, unequal opportunities for students through biased or incomplete data, and the lack of informed consent for both parents and students. Rather than being prescriptive, the aim of the session is to share thoughts and opinions and find potential solutions for some of the potentially less desirable side effects of data use in schools. 

This session explores how an Instructional Maturity Model can help schools move toward a more sustainable approach. By developing AI-resilient units, offering practical options for using AI to support student learning, providing tools and scaffolds for assessment design, and building capability to collect and interpret evidence of student process and progress, schools can create a more robust and future-ready learning environment.