How to Use Learning Analytics? 3 Value-add Cases
As corporations become more data-driven in their decision making, learning & development has to follow suit. To make better decisions, you naturally need to collect a lot more learning data. But that alone isn’t enough. You also need capabilities to analyse the data to understand what it means. While there’s a lot of ambiguity about corporate training analytics and some organisations intentionally try to make it sound extremely difficult, it’s not entirely true. To clear out some of that ambiguity, here are 3 different use cases for learning analytics that are applicable for organisations of all sizes.
1. How to use learning analytics to increase engagement?
One of the bottleneck issues in corporate learning today is engagement. It’s not always an easy task to put out learning experiences that resonate with the learners and keep them engaged. Naturally, your content has to be of good quality, and you should likely use a fair bit of interactivity. But once all that is said and done, you should unleash the analytics.
Through learning content analytics, we can get a much better understanding of our users. We can see what are the pieces of content that are used the most or the least. We can also get an understanding of ‘when’ and ‘where’ learners tend to drop off, which then enables to start figuring out ‘why’. Furthermore, we can drill down to each interaction between the learner and content/instructors/other learners to really understand what is working and what is not. All of this (and a fair bit more!) enables us to constantly develop our learning experiences based on real information instead of gut-feels and opinions. And when we can make our content to be more relevant and to-the-point, a lot of the engagement tends to come naturally.
2. How to use learning analytics to personalise learning experiences?
Our professional learners – the employees – come with various skills, degrees of experience, education and backgrounds. As they certainly don’t represent a one-size sample, we shouldn’t be putting them through one-size-fits-all learning experience either. As organisations have understood this, the hype around personalised learning has grown significantly over the past few years. But it’s not just hype, there’s real value to personalisation that learning analytics can help us to unlock.
First of all, learning analytics help us to understand the different individuals and groups of learners in our organisation. By being able to drill down all the way to the level of individual’s interactions, we can understand our learners’ needs and challenges much better. This enables us to cater to their various strengths, diverse learning history and varying interests. Instead of providing a simple one-size-fits-all learning experience, we can use this information to design personalised learning paths for different groups or even up to an individual level. These learning paths can branch out and reconnect based on difficulty of content, experience, current job and various other factors. The learning experience thus becomes a spider’s web instead of a straight line, and you’ll be able to catch much more of your learners.
3. How to use learning analytics to prove the impact of learning?
Proving the impact or the ROI of learning is something that L&D professionals often struggle with. One of the reasons for struggle is not using learning analytics. For learning results in terms of knowledge acquisition, a data-driven approach beats out the traditional multiple choice testing or feedback forms by a long shot. Furthermore, it enables a much more formative way of assessment, thanks all the data points collected and available.
But simple knowledge acquisition isn’t simply enough to demonstrate corporate learning impact. After all, what’s the learning good for if no one applies it? Thus, it’s imperative that we combine learning analytics with performance metrics and indicators. By doing this, we’ll get a lot closer to real learning results. E.g. how did the sales training affect the sales staff routines, behaviours and performance? How much of the risky behaviour did the compliance training help to eliminate? Is our training on team management actually resulting in teams being managed better? By enabling this level of analytics, you can answer a lot more questions. Furthermore, you can also start asking questions that you were not even aware of.
In our work, learning analytics and data-driven approaches play a big part. While technology plays a big part, there’s obviously more to it. For instance, you want to be sure that you’re setting your corporate learning objectives to enable this. If you’re looking to move into more data-driven learning strategies or understand your training impact better, we can probably help you. Just reach out to us here.