What Is Learner Analytics?

Learner analytics seeks to understand more about a specific person or group engaged in activities where learning is one of the outputs. In other words, this means looking at L&D data from the perspective of what people are doing and how they perform. And this post explains different types of learners and how you can apply learning analytics to better understand and support each one.

Before we dive in, it’s important to remember that learning analytics has two dimensions:

  1. Complexity—or the sophistication of the analytics that ranges from basic measurement and data evaluation to advanced evaluation and predictive and prescriptive analytics
  2. Category—which identifies the specific area or type of learning data that’s being analyzed and covers learner analytics, learning program analytics, and learning experience analytics

Get to Know Your Learners

So what constitutes a learner? A learner might be a company employee or an anonymous external user interacting with a public learning program. Or, a group of learners might be a department within a company, a cohort going through a training program together, or those in a particular job role who need to complete a set of compliance training.

How Can Learner Analytics Help L&D?

Learner analytics often answer questions about usage patterns and performance for specific learners, such as:

  • Who is training the most often?
  • Has everyone in a particular group completed compliance training?
  • What skills does this person/group have? Are there gaps, and where?
  • Who needs development? Who are your high-potential employees? And what topics are these learners interested in?

As a result, these analytics help companies better understand their people, are used for talent management and identification, and are often tied to job certifications and personal development plans.

How to Apply Learner Analytics

Perhaps you’d like to understand more about a group of learners attempting to attain a five-step certification. Here are some specific examples of how you can apply all four levels of complexity for learner analytics.

1) Learner Measurement

Track progress through each step of the certification path. Monitor how much time each learner spends in each step.

2) Learner Evaluation

Show a chart of learner progress broken down by department. Are there departments that are falling behind? With further investigation, can you identify why these departments are behind and how to help them get back on track?

3) Advanced Learner Evaluation

Run the data about the first step of the certification path and the overall completion status through regression analysis. Are there relationships between variables you can measure during the first step and learners who ultimately drop out of the program?

For example, should you focus on creating a more engaging first impression to encourage learners to stick with the program?

4) Predictive Learner Analytics

Using Bayesian probability, look for a relationship between time spent on each certification step and the likelihood of dropping out of the program. Predict which students are most likely to drop out and identify interventions to help them continue to achieve the certification.

Up Next: Learning Program Analytics

Next, we’ll discuss learning program analytics, which helps you understand how an overall learning program performs.

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eGuide: 5 Steps to Getting Started with Learning Analytics

Now that you understand the basics of analyzing learning experiences, it's time to start applying them in your own learning program. And it's easier than you might think. In fact, there’s a lot you can do with simple metrics and the data you have right now. We've also created the following guide to help you get started right now!

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