4 Higher Ed IT Trends and How Software Can Help

Emerging technologies are fueling a revolution in higher education. Virtual reality, artificial intelligence (AI), quantum computing, and more are making immersive, tech-driven learning a reality. But for CIOs and IT leaders, realizing this digital future depends on modernizing their campus networks.

Advanced solutions and architectures are needed to meet surging bandwidth demands, ensure reliability, and power data-intensive innovations. Without a high-performing infrastructure, transformative educational technologies cannot reach their full potential.

This article explores four of the top IT trends impacting higher education and examines the unique network and infrastructure demands each creates.

1. AI in Higher Education

AI is gaining traction in higher education. New AI-powered edtech tools to enhance teaching and learning are emerging at a rapid pace. The AI in education market is projected to reach $28 billion by 2030.^Footnote1

From AI teaching assistants to intelligent tutoring systems, AI-driven education applications leverage complex algorithms to personalize instruction, provide real-time feedback, and improve student outcomes.

Adaptive learning platforms like Knewton or Thinkster Math use AI to create data-driven instruction, tailoring lessons to each student's strengths and weaknesses. Higher education institutions rely on AI teaching assistants to answer common student questions and free up faculty for higher-level instruction.

As AI becomes integral to more campus technology stacks, it creates new demands on institutional networks.

• Large volumes of data must be transmitted from devices and learning software to cloud systems for analysis by AI algorithms. This strains network bandwidth.
• AI systems are latency-sensitive. Delayed data transfer caused by network congestion or bottlenecks can significantly impact performance.
• Continuous AI model updating requires reliable uptime and network availability. Downtime severely limits functionality.
• There are heightened security risks associated with collecting and transmitting large amounts of sensitive student data.

Network management platforms such as Auvik provide visibility into traffic utilization, performance metrics, device statuses, and the configurations needed to support the use of AI tools.

• Traffic analytics ensure networks have the headroom to meet the bandwidth requirements of AI tools.
• Custom threshold alerts immediately identify latency or packet loss issues that can disrupt AI applications.
• Dependency mapping clarifies the network infrastructure that AI applications depend on to improve uptime.

With comprehensive monitoring and robust automation, network management solutions enable IT teams to optimize performance, security, and reliability for AI learning initiatives.

2. Big Data and Data Privacy

Higher education institutions are aggregating and analyzing massive amounts of data to enhance the student experience. Data informs decisions across nearly all campus operations—from admissions and advising to learning analytics. The global big data market in education is expected to reach $57.14 billion by 2030.^Footnote2

Vast amounts of student data are collected from campus learning management system (LMS) platforms, student information systems (SIS), customer relationship management (CRM) systems, and other platforms. Learning analytics leverage this data to predict student outcomes using engagement patterns, assignment scores, demographics, and other metrics. This enables targeted, early interventions to improve retention and completion rates for at-risk students.

Handling exponentially growing education data introduces network and security challenges.

• Ample network bandwidth and minimal latency for responsiveness are required to collect and transport huge datasets.
• Data lakes and analytics systems must seamlessly share data across data centers, campuses, and the cloud.
• Strict regulations like FERPA govern the privacy of student data. Data breaches can lead to noncompliance and legal consequences.
• Student records and intellectual property represent prime targets for hackers. Improving security is imperative.

Network management solutions enable IT teams to navigate big data demands while ensuring security.

• Performance monitoring tracks utilization, latency, and jitter to ensure reliable data delivery.
• Role-based access controls regulate data flows and enforce security policies.
• Anomaly detection identifies abnormal traffic that could signal an attack or breach.
• Change tracking provides visibility into network alterations that may impact security.

With end-to-end network visibility and policy-based controls, IT teams can harness the power of big data analytics while safeguarding student privacy.

3. Learning Analytics

Learning analytics refers to measuring, collecting, analyzing, and reporting student data to better understand and optimize learning environments. As data-driven education gains traction, the global learning analytics market is predicted to reach $84.7 billion by 2028.^Footnote3

Learning analytics leverage big data and AI to derive key insights from the wealth of available student data. Tracked metrics can include grades, assignment scores, learning software usage, lecture engagement, and more. The insights reveal patterns that can inform teaching practices, predict student outcomes, and enable early interventions for at-risk students.

However, scaling learning analytics introduces network hurdles.

• The collection of vast activity data across systems strains network bandwidth during transfers.
• Timely analysis and rapid interventions depend on consistently low network latency.
• Student performance data must be transmitted securely to ensure privacy.
• The analysis of data across cloud apps, data lakes, and tools requires complex integrations.
• Ongoing data growth must be accommodated in the long term.

Network management platforms offer comprehensive control and visibility to smooth learning analytics deployments.

• Traffic monitoring ensures sufficient bandwidth as data volumes scale.
• Custom thresholding provides alerts before latency disrupts analytics.
• Dependency mapping highlights critical analytics systems for reliability.
• Segmentation secures data flows for regulatory compliance.
• Change tracking enables the auditability of moves, adds, and changes.

Network management solutions help unlock the immense power of data for student success by enabling scalable, high-performance learning analytics.

4. Blended Learning Environments

Blended learning is fast becoming the norm in higher education. This approach combines online and in-person instruction, giving students flexibility in how, when, and where they access learning materials. LMS platforms like Canvas and Blackboard allow students to view video lectures, discuss course topics, submit assignments, and take quizzes online. Class time focuses more on hands-on activities, group work, and discussions.

As hybrid models become mainstream, the global blended learning market in higher education is predicted to grow at 10.3 percent CAGR until 2029.^Footnote4

Supporting blended learning environments creates potential network challenges.

• Bandwidth must accommodate videoconferencing, lecture streaming, and online collaboration.
• LMS solutions are mission-critical apps that must deliver reliable performance.
• Enabling multimedia access for remote, Wi-Fi, and cellular users is difficult.
• Usage peaks when students are online can strain network capacity.
• Uptime and consistent performance are imperative for always-on learning.

Robust network management platforms enable smooth blended learning.

• Historical utilization reports establish bandwidth needs for hybrid learning.
• Custom alerting warns of performance issues before they disrupt classes.
• Dependency mapping pinpoints critical LMS infrastructure for reliability.
• Wireless analytics ensure remote students stay connected.
• Change validation simplifies testing infrastructure improvements.

By providing unified visibility into utilization, uptime, Wi-Fi, and more, network management solutions empower IT teams to deliver robust hybrid learning at scale.

Modernizing Networks to Power the Future of Teaching and Learning

Emerging IT initiatives like AI, big data analytics, and blended learning models are transforming higher education. But technology is only half of the equation. Realizing the full potential of these technologies requires modern, high-performing campus networks that can meet soaring bandwidth demands while ensuring 24/7 reliability and security.

Powerful network management platforms like Auvik provide the comprehensive visibility, traffic analytics, network monitoring, and multi-vendor automation essential for managing today's complex education networks. By leveraging these solutions, IT leaders at colleges and universities can confidently roll out innovative new technologies to create truly next-generation learning experiences on campus.

The future of education will be defined by technology. Forward-thinking IT teams have a tremendous opportunity to be strategic partners in the digital advancement of their institutions. With the right foundational network management technology, IT teams can help usher in an exciting new era of teaching and learning.

Notes

1. AI In Education Market Size & Share Report, 2022–2030, research report, (San Francisco, CA: Grand View Research, 2022) Jump back to footnote 1 in the text.↩
2. Big Data Analytics in Education Market, research report, (Portland, OR: Allied Market Research, 2021). Jump back to footnote 2 in the text.↩
3. Benzinga, "Education and Learning Analytics Market to Show Impressive Growth of CAGR of 19.3% during the period 2023-2030," press release, September 18, 2023. Jump back to footnote 3 in the text.↩
4. Blended Learning Market, research report, (Maharashtra, India: Straits Research, November 2021). Jump back to footnote 4 in the text.↩

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Nolan Greene is Product Marketing Manager at Auvik.