Learning Analytics seem like a bit of a dark art, a mystery. You’ve read the theory, but how does it actually work in practice? It’s this question that inspired us to conduct our Learning Analytics Research Study so we could better understand how our clients are implementing these analytics in practice.
We aimed our research efforts at placing these organizations on the learning analytics triangle as a measure of maturity, and creating an overall picture of how real people are doing real learning analytics. As a result, this blog series is meant for those beginning their learning analytics strategy, are well underway, or are somewhere in between.
- Corporate Learning Analytics in Practice [Introduction]
- Learning Analytics Analytics
- Learning Analytics Categories
- Learning Analytics Dimensions, Learner Analysis
- Learning Analytics Dimensions, Learning Program Analysis
- Learning Analytics Dimensions, Learning Experience Analysis
- Complexities & Analysis Types
- Data Evaluation Complexity
- Advanced Evaluation Complexity
- Predictive & Prescriptive Complexity
- Experience Types
- Report Types
Along the way, we discovered some key findings about how clients use these analytics to not only report on and share information about their learning programs and initiatives, but also show L&D’s impact across the organization.
So, whether you’re just getting started or have a more advanced learning analytics strategy in place, here are a few important things to keep in mind.
You have options. There are more than 100 ways to slice learning analytics, so find at least one more new approach that can add value to your organization. (Tip: Don’t forget to take inspiration from others.)
Build the foundation first. Start with the basics that people expect from the reporting (e.g. completions), but don’t stop there. Even within data evaluation, there’s a lot you can do beyond completion and utilization, but few organizations are doing so. In fact, most organizations are still at the Data Evaluation level of complexity, but some are starting to trail-blaze into Advanced Evaluation. So, consider how you can gather additional data about engagement and other areas of analysis for deeper insights. (Tip: Check out the following worksheet as a starting point.)
Use the right reports and visualizations. For example, reporting on data about learning programs lends itself to specialized learning program reports (e.g. Watershed’s program report), rather than more generic visualizations (e.g. pie or bar charts).
One size does not fit all. There’s a distinction between how the L&D team uses reports compared to how managers use them. In other words, know your audience and design reports and dashboards for different stakeholders. For instance, learner reports tend to be used less than the other categories, so they may not be the best place to start your learning analytics journey. Start with learning program reports for managers, or learning experience reports for the L&D team and content creators.
Location, location, location. Try using reports that compare learners in multiple locations. These reports may get more traffic than you think, as these reports may help viewers identify how well learners in certain locations are performing or how active learners are by location.
Don’t forget about seasonal patterns. Report usage levels vary depending on the time of the year—such as key dates, deadlines, or year end—so expect numbers to fluctuate.
Don’t let reports go stale. Keep them up to date to ensure stakeholders remain engaged.
Learning happens everywhere. Chances are, there’s untracked learning happening somewhere in your organization. If you don’t think you can track it, think again. Remember, we’ve seen organizations tracking all kinds of learning experiences—so, if they can track them, you can too!
Learning analytics made easy.
Download this checklist to start tracking the analytics you already have in place and where you’d like to go next. Continue your journey by downloading this eBook, which walks you through the five steps to start using learning analytics.