Learning analytics is 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.
Most accepted definitions refer to analytics in the education space, not the corporate space. At Watershed, we’ve built on these definitions to create a description for these analytics in the corporate space. The key part of this definition is the second half. In a corporate context, learning analytics ultimately serves the purpose of improving organizational performance.
The categories of learning analytics
There are many types of learning analytics and things you can measure and analyze. We segment these analytics into three categories: learning experience analytics, learner analytics, and learning program analytics.What is learning experience analytics?
Learning experience analytics seek to understand more about a specific learning activity. The learning experience category often answers questions about usage patterns for a specific activity, such as:
- How much is it being used?
- Is there “scrap learning” that isn’t being used?
- When is it being used and for how long?
- What resources or topics do learners search for most?
- How do learners navigate the experience?
[Read More on Learning Experience Analytics]
What is learner analytics?
Learner analytics seek to understand more about a specific person or group of people engaged in activities where learning is one of the outputs. It covers questions about usage patterns and performance for specific learners, such as:
- Who is training the most?
- Has everyone in this group completed compliance training?
- What skills does this person/group have? Where are the gaps?
- Who needs development? Who are high-potential employees?
- What topics are these learners interested in?
[Read More on Learner Analytics]
What is learning program analytics?
Learning program analytics seek to understand how an overall learning program is performing. A learning program typically encompasses many learners and many learning experiences (although it could easily contain just a few). Learning program analytics answer questions about learning’s strategic impact on the business, such as:
- Do learners behave differently after completing training?
- Has organizational performance improved because of learning?
- Has this method of learning saved the company money?
- Which learning methodology is more effective?
[Read More on Learning Program Analytics]
The levels of learning analytics
We define four levels of these analytics: measurement, evaluation, advanced evaluation, and predictive and prescriptive analytics. Although each of these levels are correctly referred to as analytics, they mean vastly different things in terms of complexity, difficulty, and power.
Analytics start with measurement, or the simple act of tracking things and recording values to tell us what happened. Measurement doesn’t require complicated math or statistics, but you must start by gathering data. Otherwise, it’s impossible to do any analytics.
Once the data has been captured, it's time to start to evaluating it and assessing whether the data means something good or bad. At this level, we’re applying high-school level math—averages, means, modes, and basic statistics—to aggregate the data and establish benchmarks. In current practice, most analytics fall into the basic data evaluation category, and that’s OK. There’s tremendous value here, and opportunities for some huge wins.
Exciting things start to happen as we get into advanced evaluation and apply college-level math. Here, we’re looking at things such as correlations and regression analysis. We’re applying statistical techniques to understand, not just what happened, but why it happened. Advanced evaluation creates theories about causation, allowing us to focus on what works best and scrap ineffective learning.
Predictive & Prescriptive Analytics
The most sophisticated levels of analytics are predictive and prescriptive analytics, which require graduate-level math and often rely on AI or machine learning powered by big data sets. Predictive analytics say, “based on what’s happened in the past, here’s what is most likely to happen next.” Prescriptive analytics take that a step further and say, “based on what’s most likely to happen next, here’s the action we should take to optimize the outcome.” Ultimately, when we get here, we rely on highly intelligent recommendation engines that deliver just the right learning, at just the right moment, in just the right way to significantly improve performance. As an industry, we’re not there yet, but we can get there if we start measuring and work our way up.
Combining Categories & Complexity
Learning analytics don’t have to be complicated. Start with the basic categories and complexities that comprise these analytics and work your way outward. When these categories and complexities are combined, they form the matrix—or pyramid—of learning analytics.
Many organizations master the lowest level of complexity within a specific category and then work their ways outward. And some organizations may reach high levels in one category, but reach lower levels in other categories. As long as you identify where you fall within the analytics pyramid, you’ll know where you’re going—which helps you set goals, determine metrics, and evaluate the maturity of your program.
Hear from your peers.
Read tips from practitioners who have implemented various levels of learning analytics in their organizations.
"Be relentless in your in your vision and flexible with the details of how you approach understanding and improving a culture of learning."
Gordon Trujillo, Visa [Read More]
"Instead of building Excel reports on a full-time basis, our analyst gets to probe the data to uncover stories and experiment information."
Andy Webb, Applied Industrial Technologies [Read More]
"You have to pick your battles and understand what is critical to know and report and what isn't relevant to moving the needles."
Monica Griggs, Bridgestone [Read More]
"The better we understand the process as applied in reality, the better we can evaluate our data and see if we are missing anything."
Amir Bar, Halliburton [Read More]
What is a Learning Analytics Platform (LAP)?
Similar to an LRS, an LAP typically takes advantage of xAPI to aggregate data about all of the training and learning events across your organization. However, an LAP takes it a step further by applying sophisticated reporting and analytics capabilities to that data.
1) It isn’t an LMS.
2) It isn’t really a BI tool.
3) And, it isn’t just an LRS.
Read more about how an LAP works alongside an LMS or BI tool on the Insights blog.
Want to get started with learning analytics?
Download our eGuide that explores the five steps to start using learning analytics, covers different technologies that support a learning analytics programs, and shares approaches to consider.