Personalised classroom learning: Are we there yet? - Education Matters

Personalised classroom learning: Are we there yet?

Acer personalised classroom learning

Darren Simmons, Oceanic Managing Director at Acer, discusses how technology has the potential to enable greater levels of personalised classroom learning, assisting educators to better understand the needs of each student.

Every child is different, yet in many ways the traditional classroom environment treats them largely the same. This is by no means the fault of educators themselves but rather is a by-product of the modern education system: one-to-many teacher-student ratios, broad-based curricula, and so on. Educators know that the more they can personalise the learning experience for those they teach, the more they’ll learn and grow – it’s just not easy doing so.

Technology promises to enable far more personalised learning in the classroom, giving teachers the capacity and capability to understand each student’s strengths, pain points and learning behaviours more than ever before. For that to happen, however, educators need to develop their own understanding and proficiency in these technologies – and be ready to not only deploy them into their classrooms, but also translate what the technology says about each student into a more personal, empathetic level of care.

Drawing attention in the classroom
Technology can reveal a lot about how humans behave and conduct themselves – including many things that may not be observed otherwise. When applied to students, those observations help both them and their teachers to more clearly identify what learning strategies resonate the most, potentially leading to much more effective study plans and classroom participation. It’s worth stressing that none of this is new to experienced educators: they observe and guide their students all the time. However, technology allows them to cover far more students, with far more attention, than they ever could before.

Right now, Acer is supporting a major research project being conducted by the University of Technology Sydney (UTS), which involves collecting and analysing biometric data from student devices – tracking their eye movements, gestures, keystrokes and other physical indicators of attentiveness during lessons. Our hope is that by analysing this data with artificial intelligence (AI) and machine learning, we’ll be able to derive correlations between behavioural patterns and learning outcomes. That should then inform how educators engage with and personalise learning plans for different students – both primary and secondary – as well as improving the effectiveness of curriculum materials more broadly.

The insights from technologies like the ones we’re trialling with UTS will not, in themselves, solve the learning issues that different students might face. Nor do they necessarily give us definitive answers into what causes a student’s attention to waver – from family or financial difficulties to bullying or learning difficulties. But these insights act as a solid starting point which educators can use to identify when a student may need help, or where certain parts of a lesson may not be working as well as it should. From there, educators can talk to and work with students to better understand and address any potential issues – and, in the process, give AI and machine learning technologies greater context to help in picking up similar issues in the future.

We’re not there just yet
As we embed digital technology more into our lessons and classrooms, we’ll gain more data with which to understand our students and potentially personalise their learning experience. Analysing eye movements and gestures like we’re doing with UTS may be relatively new ground, but many schools are already analysing digitised test scores, assignment responses, and even how students collaborate online to gain deeper appreciation of students’ unique learning patterns. Yet for personalised learning to really take hold in Australia, we’ll need to radically rethink how classrooms function; tackling fundamental issues like student-teacher ratios and curriculum design in the process.

AI can and should play an important role in education reform because it can tell us so much more about students and individual teaching approaches, than ever before. But we need to also remember that technology only takes us so far: personalised learning ultimately depends on our willingness and ability to engage with each student as an individual. For now, two questions Australian educators should be considering are: how might I use technology to better understand my students? And, more critically: how equipped am I to act on that understanding with what I currently have? We’re making bold steps in that direction as a country, but there’s still lots to be done.

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