“Learning Analytics”: What It Is and How It Works in E-Learning

What Is “Learning Analytics”?

“Learning analytics” is the process of measuring, collecting, analyzing, and presenting data about learners and their contexts with the goal of understanding and optimizing learning. According to the Society for Learning Analytics Research (SoLAR), its purpose is to understand what happens during training, why it happens, and how to improve the environment in which it takes place. In e-learning, this means turning the data generated by every interaction with the platform — logins, time spent, results, drop-off patterns — into concrete improvement decisions.

In corporate training, “learning analytics” has become a key tool for L&D teams. According to the LinkedIn Workplace Learning Report 2024, 89% of learning leaders consider demonstrating the impact of their programs a priority, but only 27% measure the ROI of their initiatives systematically. “Learning analytics” is the discipline that closes that gap: it connects learning data with real business indicators.

How “Learning Analytics” Works in E-Learning

“Learning analytics” operates in a continuous four-step cycle: data capture, storage, analysis, and action. In an e-learning environment, most of the data is generated automatically inside the LMS: every time an employee accesses a module, completes an activity, takes an assessment, or drops a course halfway through, that action is recorded.

This data is processed at two levels. At the individual level, it enables training tracking for each employee: what they have completed, where they struggle, and what they need reinforcement on. At the aggregate level, it reveals global patterns: which content causes the most drop-off, at what point in the course attention is lost, and which employee groups show skill gaps. By combining both perspectives, the L&D team can make evidence-based decisions instead of relying on intuition.

The most common technical standard for capturing data in e-learning is xAPI, which allows learning activities to be recorded beyond the LMS. For organizations working with SCORM — the classic standard — the LMS itself already collects enough essential metrics to start applying analytics.

What Data Does “Learning Analytics” Measure?

Not all data holds the same value. “Learning analytics” works with three main categories that, combined, offer a complete view of the training process:

Levels of Analysis: From Descriptive to Predictive

“Learning analytics” is not a single technique but a spectrum of capabilities ranging from the most basic to the most sophisticated. Most L&D teams start at the descriptive level and progress gradually as the program matures and more tools become available:

Standards That Make “Learning Analytics” Possible

For “learning analytics” to work, learning data must be captured in a structured, comparable way. In the e-learning ecosystem there are two main standards:

• xAPI (Experience API): allows any type of learning activity — inside and outside the LMS — to be recorded through “actor + verb + object” statements. The data is stored in an external repository called an LRS (Learning Record Store), enabling much richer and more flexible analysis.
• SCORM: the classic, widely adopted e-learning standard. More limited than xAPI in the variety of data it captures, but sufficient for fundamental analytics: completion, score, and time spent. It is the most common standard among organizations working with a conventional LMS.
• Caliper Analytics: a standard developed by IMS Global for academic contexts, with a more structured metrics framework than xAPI, though less widespread in the corporate world.

For most organizations with structured corporate training, SCORM combined with native LMS analytics offers a solid starting point without the need for additional infrastructure.

Benefits of “Learning Analytics” for L&D Teams

Applied systematically, “learning analytics” transforms the way training teams design, adjust, and justify their programs:

• Data-driven decisions, not intuition. Instead of assuming what works, the team can see exactly which modules generate engagement and which don’t, and adjust content accordingly. LMS learning reports are the first step toward turning data into real improvements.
• Early identification of skill gaps. Before a performance issue reaches the business, analytics can detect it in the training data. Seeing which L&D metrics correlate with performance allows the team to act preventively.
• Learning personalization at scale. With context data (role, area, history), it’s possible to recommend tailored learning paths to each profile without manual management. Learning outcomes align better with each employee’s actual needs.
• Justifying training ROI. Analytics makes it possible to connect learning indicators (completion, score, time) with business metrics (productivity, turnover, sales), enabling objective e-learning evaluation.

Risks and Limitations: What “Learning Analytics” Can’t Do Alone

“Learning analytics” is a powerful tool, but it’s not infallible. Before implementing it, it’s worth keeping its limitations in mind:

• Data quality: if the training isn’t well designed — modules without assessments, uncontrolled access, content without proper SCORM structure — the data collected will be superficial or misleading. Analytics amplifies what’s already on the platform; it can’t compensate for poor instructional design.
• Privacy and GDPR: learning data is personal data. Its collection, storage, and use must comply with the European General Data Protection Regulation. Organizations must clearly define what data is collected, for what purpose, and for how long.
• Human interpretation: data alone doesn’t decide anything. A high drop-off rate can indicate boring content, a confusing interface, or simply that employees find the module too long to complete in one sitting. Data needs context to become sound decisions.
• Risk of vanity metrics: optimizing for completion or scores isn’t the same as optimizing for real learning. An employee can pass a test without having internalized the knowledge. Analytics should be complemented with a broader training evaluation that includes transfer to the job.

“Learning Analytics” and Training ROI

One of the most strategic uses of “learning analytics” is demonstrating the economic value of training investment. When learning data is cross-referenced with business indicators — productivity, time to autonomy, error reduction, retention rates — it becomes possible to build a quantifiable case for L&D’s impact.

The most widely used model for this purpose is Kirkpatrick’s (four levels: reaction, learning, behavior, results), which requires exactly the type of data analytics collects: participant satisfaction, assessment results, behavior change, and business metrics. To dig deeper into how to rigorously measure training ROI, it’s essential to have a data collection strategy from the program’s design stage.

“Learning Analytics” in Practice: From Data to Training Improvement

MAPFRE is a great example of how analyzing training data can transform a program’s effectiveness at corporate scale. With staff spread across multiple countries, the insurer used isEazy to centralize and analyze data from its training programs, identifying which learning paths directly impacted sales indicators. Discover how they did it →

How isEazy LMS Turns Data into Training Decisions

isEazy LMS includes a built-in analytics dashboard that gives training managers real-time access to their programs’ key metrics: completion rates, results per module, group comparisons, and trends over time. Data is collected automatically through SCORM with no additional configuration needed, meaning any course created with isEazy Author generates analytics data from the very first access.

If you want to see it in action, explore isEazy LMS and discover how the most demanding L&D teams turn learning data into more effective training programs.