The onset of the pandemic has given rise to adaptive learning, a concept that’s reshaping the classroom and improving education. Adaptive learning involves using a personalized, tech-based teaching method. This model makes sure that students have equal opportunities to succeed despite any differences in learning style, pace and preferences.
With adaptive learning systems in place, a student’s performance and reactions to digital content can be analyzed in real time, and lessons modified based on that data. For example, if a student continues to struggle with a particular subject or lesson, the network will “take note” and adjust the learning experience to meet the student’s individual needs.
Adaptive learning systems combine multiple technologies, including artificial intelligence (AI), streaming and archived video, immersive mixed reality, and gamification systems. But the simultaneous use of these bandwidth-intensive applications places greater strain on the network and the increase in remote learning, educational and home networks. Educational institutions and home classrooms that are unprepared for these traffic surges can experience unplanned network congestion or even outages — often at the worst possible moment, like during online exams.
For adaptive learning to thrive, the underlying communication network must be programmed to automatically adapt to changing end-user needs.
Adaptive learning applications are typically housed in a school district’s data center or public cloud. They depend on a network that is fast, resilient and reliable so students can access the applications from anywhere at any time. When there is bandwidth congestion, latency issues or major outages, students can face lost instruction time that can impact their performance. In a survey from the Center for Digital Education, nearly one-third of K-12 district respondents said that concerns about the reliability of their networks keep them up at night.
With a programmable network infrastructure, the network is transformed from static to dynamic with a layer of software intelligence. This layer watches the telemetry from the network infrastructure and can make decisions in real time to prevent disconnects, dropouts, congestion and latency. A programmable infrastructure requires a programmable fabric with adaptive learning so that it can reroute connections and capacity as needed and help avoid delays or lag in the user experience. It allows the network to adjust using real-time performance data and to be reconfigured as needed to support adaptive learning applications running on top of it. This ensures students and teachers are not only connected but also benefiting from a more immersive and engaging learning environment.
A significant amount of data is created in a programmable infrastructure. This “big data” can be used to reveal trends about resource consumption, traffic patterns, vulnerabilities that might cause delays or lags in connectivity and more. With this information, the network can automatically learn and adjust to changing needs over time. Essentially, the network can turn a high volume of data into actionable insights that instruct the network to automatically adjust as needed. The network also provides small data, which are those simpler moments such as a customer's (in this case, an education institution) need for additional network capacity to cover an event, like a combined group lesson. These events require swift network responses that can be made with robust analytics.
With that information, network providers and data center operators can run data-driven policies that react securely to the user's needs in real time. Once the decisions are made, a human operator — or better yet, automated systems using predefined policies — can step in and approve or change things as necessary to optimize the network.
Human error is the leading cause of network downtime. A 2018 survey on server reliability by the Information Technology Intelligence Corps found that human error is responsible for 58% of network downtime. Network analytics, intelligence and automation all help eliminate errors and improve performance when carrying out tasks like loading access controllers, provisioning routers or configuring traffic engineering tunnels to optimize transport and relieve congestion.
The ability to automate across multiple networks with software-defined control is critical to ensuring peak performance. When networks can interoperate with APIs and move data efficiently and swiftly from point to point, adaptive learning applications can run seamlessly. A network framework that can adapt allows operators to simplify network management and create end-to-end automation even across hybrid networks with multiple vendors or domains.
When students and instructors have mobility made possible by cloud-based technologies, the constraints of the physical classroom no longer create barriers to learning. For adaptive learning to take hold, students must also be equipped with the right devices, including tablets, smartphones and laptops that offer the flexibility to learn from anywhere, anytime.
Schools and other educational institutions are embracing adaptive learning. To make the most of their investment, they'll need to work with their service providers to address connectivity issues ahead of time and ensure their network infrastructure is built with software intelligence and programmability so that it can adjust to the changing demands of remote and digital learning.
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