What Is Learning Analytics? 6 Key Use Cases for 2025

Editor’s Note: This article has been refreshed with up-to-date information to ensure accuracy and relevance.

Picture this: A corporate trainer opens her learning dashboard to find that half the employees in a leadership program are consistently skipping key modules. Across town, a professor teaching a hybrid course notices her once lively discussion forums have gone silent. Both are left asking the same question: what’s going wrong, and how can they turn things around?

The solution lies in leveraging the full potential of learning analytics. By transforming raw data into actionable insights, it equips educators and trainers to pinpoint problems, implement targeted strategies, and empower learners to reach new levels of success.

As we embrace 2025, let’s explore how data-driven approaches are unlocking new opportunities for engagement, efficiency, and educational success.

What Is Learning Analytics, and Why Does It Matter?

Learning analytics involves the collection, measurement, reporting, and analysis of data related to the learner and the environment in which teaching and learning occur. At its core, learning analytics provides actionable insights into how students learn, what challenges they face, and which teaching methods are most effective. It enables educators and institutions to make informed decisions that improve learning outcomes and foster learner success.

Historically, educators and training professionals have examined areas like learner progress, teaching methods, training data, and curriculum design. However, only recently have they begun fully harnessing the potential of modern technologies.

Learning analytics now extends beyond simply predicting whether students will succeed or struggle. By leveraging vast amounts of data, advanced computational methods, and AI-driven insights, it brings several transformative benefits, including:

• Customization of learning content and pacing for individual needs
• Real-time performance tracking and immediate feedback
• The ability to address gaps in access and achievement to promote equity
• Better alignment of education with workforce trends and future skills demands
• Integration of data across platforms for a holistic perspective

Ultimately, learning analytics paves the way for a more adaptive, inclusive, and future-ready educational experience.

RELATED READING | ‘Retain to Succeed: Using Learning Analytics for Better Student Engagement’

From Data to Action: 6 Key Applications of Learning Analytics

Learning analytics continues to evolve, offering tools and methodologies to enhance education and training outcomes. The applications that follow reflect the innovative ways higher education institutions and corporations are harnessing data to address modern challenges.

1) AI-Powered Adaptive Learning

Advanced AI technologies are transforming adaptive learning systems, using learning analytics to most effectively meet individual learner needs. These systems analyze factors like prior knowledge, learning preferences, and performance data to craft personalized educational pathways that enhance engagement and outcomes.

For instance, an adaptive learning platform could analyze a student’s struggle with advanced math concepts and automatically recommend foundational tutorials to reinforce understanding. Similarly, in corporate training, an AI-driven system might align learning modules with career goals, suggesting leadership development content to a high-performing employee aspiring to management roles. Thanks to delivering real-time feedback and targeted resources, these systems help learners engage more deeply with their education and stay motivated to succeed.

2) Data-Powered Workforce Upskilling

Organizations increasingly rely on learning analytics to close skills gaps and future-proof their workforce. These tools assess current competencies, predict future skill demands, and track training effectiveness through performance metrics. Predictive analytics and AI-powered skills inference are helping employers design targeted upskilling programs, while real-time monitoring ensures timely support for employee progress. Imagine a company notices that a programming language is gaining traction in their industry. Using learning analytics, they identify the current skills gap among employees for this language and design a tailored training program featuring interactive modules and real-world coding projects. Managers can track each employee's progress in real time, providing support and guidance where necessary. After the program, post-training assessments highlight significant improvements in proficiency, ensuring the company stays competitive while fostering employee growth and confidence.

3) Boosting Engagement in Large Cohorts

Managing large groups of learners presents challenges in maintaining personalized engagement and timely interventions. Learning analytics can help address this challenge by offering insights into learner activity and enabling timely interventions. By leveraging tools such as Open LMS’s Personalized Learning Designer (PLD), it’s possible to automate engagement strategies and ensure that everyone remains on track.For example, in a large health science class of over 100 students, the University of New South Wales (UNSW) developed the Academic Success Monitor (ASM) to analyze student activity and flag those who hadn’t logged in for over a week or missed key formative assessments. These insights allowed the instructor to use tools—including PLD—to send personalized, automated reminders, leading to a significant increase in participation and assignment submissions while reducing administrative workload by 40%.

LEARN HOW PLD WORKS | ‘8 Personalized Learning Designer Rules You Can’t Live Without’

4) Course Design Optimization

Learning analytics is a powerful tool for creating adaptive, learner-focused courses. By examining how learners interact with course materials, educators and training designers can uncover underutilized tools, identify areas of confusion, and make targeted revisions to address learning gaps. These insights keep the learning modules effective and engaging throughout the entire program.

Take compliance programs as an example: analytics can pinpoint which regulatory topics learners struggle with most. If a specific module consistently receives low engagement, analytics can suggest adjustments, such as breaking content into smaller parts (microlearning), adding multimedia elements, or changing the delivery format. This method boosts learner engagement while ensuring a thorough understanding of crucial compliance concepts.

5) Enhancing Equity in Education

Learning analytics has the potential to make learning more equitable by uncovering gaps in access, engagement, and achievement. By analyzing data on participation patterns, demographics, and performance, you can identify underserved employees or at-risk students and take meaningful steps to address their needs.

For instance, in a higher education setting, analytics can identify if international students are struggling to take part in discussions or access course materials, which can signal language barriers or unfamiliarity with digital platforms. In workplace training, analytics can highlight disparities in completion rates across regions or departments, which can signal differences in resource availability or varying levels of local leadership support.

With these insights, instructors can provide personalized outreach, supplemental resources, or adapt their teaching methods to better meet diverse needs. If the issue relates to accessibility, tools like the Brickfield Accessibility Toolkit can work alongside learning analytics to automatically scan course materials for potential problems. It then generates detailed reports highlighting areas that need improvement, helping instructors create more inclusive and accessible learning environments.

6) Integrating Data Across Systems for Holistic Insights

Modern learning analytics stands out for its ability to unify data from various systems, offering a comprehensive view of learner progress and engagement across platforms. By pulling information from an LMS, SIS, HRIS, or other platform, learning analytics connects the dots to deliver actionable insights that were previously siloed elsewhere in the organization.

For example, with tools like IntelliBoard, an educator can combine attendance data from video conferencing software with assignment completion rates from the LMS to identify trends in participation and performance. Without this integration, they might mistakenly attribute low grades to a lack of effort when the real issue is inconsistent attendance caused by technical difficulties.

Similarly, corporate training programs can integrate learning data with HR systems to track how training correlates with employee retention or performance metrics. Without a holistic view, managers might allocate resources to ineffective training modules, overlooking courses that genuinely drive measurable impact. This comprehensive approach ensures that decisions are accurate and interventions are well-targeted, improving overall outcomes for learners.

MORE ON TRAINING IMPACT | ‘Measuring Impact and ROI: How a Corporate LMS Helps You Get Started’

Closing Thoughts: Ethical and Responsible Use of Learning Data

The use of certain learning analytics tools has implications for institutions and organizations committed to ethical practices. Beyond compliance with regulations like GDPR and FERPA, they must critically examine how algorithms used by certain platforms could perpetuate systemic biases. This includes auditing data inputs, ensuring transparency in AI decision-making, and prioritizing equitable outcomes. These insights position you to lead the way in ethical, responsible innovation, staying ahead of challenges while fostering a positive learning environment.

Maximize the Impact of Learning Analytics With Open LMS

To cultivate a successful learning environment, you can’t afford to ignore learning analytics. Data-driven decisions lead to greater efficiency and effectiveness, ensuring your time and resources directly address the unique needs of your students or organization. Think of an LMS as the engine of a car, ensuring the ride is smooth and efficient, while learning analytics acts as the GPS, analyzing the route, spotting detours, and guiding you to better destinations.

Open LMS further supports your success by addressing skills gaps, enabling targeted upskilling, and ensuring the responsible use of data with secure hosting solutions. It also fosters diversity, equity, and inclusion by providing detailed learner insights that help you tailor support where it’s needed most. Adaptive learning capabilities, supported by tools like PLD, enable educators to tailor experiences based on individual learner activity and performance. Together, these tools enhance individual outcomes while streamlining course management and instructional strategies.