With today’s students connecting three or more devices to the school network and many learning applications moving to the cloud, it is no surprise that networks are turning to artificial intelligence (AI) and machine learning to help deliver the best learning experience.
Mark Verbloot, systems engineering director APJ, at Aruba – a Hewlett Packard Company – said is excited about these possibilities.
He views them as the opportunity to see the impact of the ‘Digital Revolution’ first-hand, now that modern learning conditions reflect the working environment students are ultimately preparing for.
“Classrooms are changing, shifting away from rows of desks and instead opting for spaces that promote collaboration between students, teachers and devices,” Verbloot told The Educator.
“In order to support this new state of digital learning, schools and campuses require scalable networks to accommodate for a wide range of uses.”
A vast majority of learning programs in today’s school curriculums require the network to support digital learning, such as student collaboration via cloud-based apps like Google Docs and Office 365.
There are also online assessments, multiple personal student devices including laptops that house textbooks and enable research-driven lessons, as well as a host of connected IoT devices such as smartboards and presentation tools, all running off the one network.
So, in a device rich environment, how can AI be used to help promote better learning?
“By deploying artificial intelligence and machine learning across networks, we are creating environments which encourage network optimisation and help to improve network security overall,” Verbloot explained.
Like the students in the classroom, says Verbloot, the network employs machine learning and artificial intelligence for “constant testing to continuously ensure seamless, consistent performance.”
“Having the capability to zone in on user performance and specific applications in use creates a better understanding of what areas need more support and when, allowing IT staff to prioritise critical learning applications based on real-time needs, such as NAPLAN testing,” he said.
“The network itself can also learn from these insights, training itself to recognise good network behaviour and functionality to understand and maintain the ideal performance standard.”
Verbloot said it can then use this as a benchmark to make recommendations and execute changes automatically as required, resulting in a cycle of constant learning and improvement.
“The same can be applied to network security. A network leveraging machine learning and AI can learn to always be on the lookout for malicious activity and identify any evolutions in cyber risks,” he said.
“In a school environment where staff and students connect various unidentified devices and guests regularly demand access to the network, machine learning can easily profile a user and their digital behaviour to identify and flag security risks.”
With these security capabilities, Verbloot said staff and students can work in the classroom “with confidence that they are learning in a safe and secure digital environment, free from any interruptions or threats”.
“The network is essentially a sensor which actively monitors all users, devices and applications to automate enhancements and security, therefore improving the network experience for both staff and students,” Verbloot said.
“A stellar, A-grade network that understands its environment will ultimately lead to an improved classroom experience that is boosted by engaged students.”
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