Your Guide to Employee Learning Analytics + Metrics to Track

Picture this: you spend a bucket of money on a training initiative, and although employees complete the training, their behavior remains the same. Or employees complain about the training, take a long time to complete it and do just enough to get by. Sound familiar? While not every employee will enthusiastically embrace training, tracking a few key learning analytics can help you better identify training needs, train to that need better, and fully quantify results of your efforts.

From planning to post-learning reviews, here’s what you need to know about the best types of learning analytics and metrics to track for your employee training initiatives.

What are learning analytics?

The Society of Learning Analytics Research (SOLAR) defines learning analytics as “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 a mouthful, but breaking it down, it’s simple. Learning analytics is all about collecting and studying data on learners. The aim is to figure out how people learn best. By analyzing this data, we can make smart choices to make learning even better for everyone.

Learning analytics takes into account:

• The training subject
• The people who are learning
• The methods used to transmit information
• How behavior changes (or not)
• How tools are supporting efforts
• What to do when the results are not what you expected

Implementing learning analytics comes with a significant set of challenges, though. Think about it. If your company still needs help to make time for regular professional development, it can feel overwhelming to layer a new process on top of that.

But it is possible to build in learning analytics tools and tracking of key metrics from the beginning to help make the process easier. An easier process means teams are more inclined to gather data and use it to improve your learning programs. And in that scenario, everyone wins.

🤓Nerd out: Discover how the principles of adult learning theory can help you design better training for your employees.

Why they’re important to track: Learning analytics research

Ineffective training costs more money than effective training. In fact, some research indicates ineffective training costs companies $13 million for every 1,000 employees. Per capita, that cost might be even higher for smaller companies.

But companies who invest their energy into designing quality training programs supported by learning analytics? They see profits that average 24% higher than companies with lackluster or inadequate professional development.

Research has found that learning analytics also supports better professional development by encouraging self-reflection among your employees and learning teams. When people have an awareness of their strengths and their growing edges, they are better able to:

• Examine their thinking
• Engage more confidently in the material
• Set better goals

All of these behaviors not only promote personal development but help foster a growth mindset across your workplace. And that leads to the professional development results you are looking for.

Types of learning analytics to track

There are four basic types of learning analytics:

1. Descriptive: What happened?
2. Diagnostic: Why did it happen?
3. Predictive: What happens next?
4. Prescriptive: What can we do about it?

Each of these can be addressed in different stages of the training, broken down into three parts: before, during, and after.

Before training starts

As with every type of training, start with identifying your training needs. Setting up a straightforward system for managers and administrators to identify what’s needed is one of the best ways to start.

The other is to look at what training has already occurred. Check in with managers and employees to see what was successful and what needs tweaking. This is a more targeted approach than just throwing subject matter at employees.

A good learning analytics example here is safety training. It is easy to see what's not working if employees continue to get injured on the job. Taking the time to identify what training has been delivered and which areas are ineffective gives course designers something to focus on.

Another critical piece of learning analytics that occurs before training starts is getting to know employees. In some cases, training doesn't work because it's delivered in a way that employees need help understanding. This has to do with everything from education level to cultural background and is essential to creating effective training.

Ready to go in-depth? A full training needs analysis can support your work in this step.

During training

Using digital training tools such as a Learning Management System (LMS) or a Learning Experience Platform (LXP) to deliver training makes it much easier to collect and use learning analytics. You can track behaviors, including:

• Who signs on to training, and when
• What pages of the training they skip
• Which training items are incorrect (or correct) most often
• The completion and engagement rates of your training

If you utilize branching scenarios, simulations, or gamification, you can also see how employees think and make decisions. If everyone is problem-solving in the wrong direction, that’s something to address when you adjust your training in the future.

After training

Many people are familiar with this aspect of learning analytics. After all, it's easy to get a printout of who has completed which online multiple-choice quiz and who has not. It's also easy to track employee time-on-task.

But it’s so much more than that. How does that 100% on an online quiz translate to employee performance? Does it result in a bump in sales, or are the numbers flat? Are there more accidents at work or fewer in the targeted area?

Sure, the numbers matter in figuring out who has actually participated in the training. But the more telling result is how that translates into the work. This information is what you’ll use to identify what you need to change in your training to get the results you’re looking for.

The four metrics you’ll use to determine effectiveness at this stage are:

1. How long it took for employees to learn the material
2. If employees retain the new knowledge or skill
3. If employees apply the new knowledge or skill
4. How employees felt about the training itself

How to get the most out of your data

Using the right tools to collect data to analyze is key. These include:

• A great learning management system (LMS): This can track time-on-task, logins, and digital evaluations.
• xAPI: Focusing on harder-to-quantify metrics like how an employee applies the new knowledge, xAPI integrates seamlessly with other LMS, moves between platforms, and even tracks training completed outside the LMS.
• Mobile platforms: These trigger location-based learning that can be applied on the job and tracked over time.

But great data is only the beginning. All data analytics includes data analysis, but not all data analysis is analytical. You aren’t looking for one answer in one set of numbers (data analysis). You’re looking at the breadth of data from beginning to end, all aimed at improving your training initiatives.

Let’s get started

Learning analytics is about action that starts before the first employee logs on to the first training module. It requires focus on a goal identified by the people most affected by the outcome.

At EdgePoint Learning, we know how to track the numbers that matter for creating true behavior change in your employees. We can help you set up actionable tracking measurements for your full eLearning program, or advise on best practices. We know what it takes to get great results from your training. Get in touch today!