Transform L&D Programs: An In-Depth Introduction to Learning Analytics

This blog series will shed light on the exciting—and sometimes confusing—topic of analyzing learning. We’ll also outline a model for talking about the different types of learning analytics and describe the various levels of sophistication in analytics.

What is learning analytics?

Even though it’s a popular topic, there doesn’t seem to be a standard definition or explanation. For instance, one commonly used definition of learning analytics is:

the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs

That’s not a bad place to start, but there’s still a lot of disagreement and confusion about what the term actually means. Complicating things further, most of the existing literature defines it in an academic context with subtle but significant differences from how it’s used in a corporate context.

Corporate learning analytics: Let’s get down to business

In the corporate context, the purpose of learning is to develop people to perform better in their jobs, to prepare them for the next steps in their careers, and to ensure the long-term success of organizations. “Learners” are often better described as “employees” or sometimes even “prospective employees.”

Learning programs exist to benefit organizational performance, which, in turn, impacts business outcomes. That’s why improving business outcomes is a critical priority for most business leaders (i.e., the people who sign your paycheck). And analytics about learners, programs, and experiences link learning with individual, team, and organizational performance.

We suggest that the definition of corporate learning analytics is something more like this:

the measurement, collection, analysis, and reporting of data about learners, learning experiences, and learning programs, for purposes of understanding and optimizing learning and its impact on an organization’s performance

Key takeaways

During the following weeks, we’ll explore the world of analyzing learning and discuss applications, predictive and prescriptive analytics, and learning experience analytics, to name a few. These topics include:

• What Does “Learning Analytics” Actually Mean?
• Learning Analytics & Complexity
• Learning Experience Analytics
• Learner Analytics?
• Learning Program Analytics
• What’s a Learning Analytics Platform?
• Learning Operations Analytics
• 5 Steps of Learning Analytics
• Learning Analytics Explained [Recap]

You’ll also hear from L&D practitioners across a variety of industries. You’ll see how they apply learning analytics in their organizations, along with their tips for getting started.

• Chipotle Mexican Grill
• Applied Industrial Technologies [Part 1]
• Applied Industrial Technologies [Part 2]
• Halliburton
• Bridgestone Americas
• Visa's Digital University

Up Next: What does learning analytics really mean?

Our definition of corporate learning analytics is a good start—but to really understand what this term means, we need to dive deeper into how different people use it. So, in addition to explaining this term in more detail, our next blog post explores the complexities and categories of learning analytics and how they are tied to our learning analytics maturity model—The Watershed Method.