What Is Learning Analytics?

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.

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 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.

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.