What Does "Learning Analytics" Actually Mean?

    
We covered the basics of learning analytics in our last post. Now, we’ll explore how people use this term and introduce you to the Watershed Method™ for learning analytics.


Our definition of corporate learning analytics is a good start, but to really understand what this term means, we need to dive deeper into how different people use it. 

There are two dimensions in which people use the term. The first dimension is complexity, or the sophistication of the analytics. The second dimension is category, which identifies the specific area or type of learning data that’s being analyzed (which answers questions about the experience, the learner, or as a whole program).

We've created the Watershed Method, which defines these degrees of complexity by breaking them into four levels and identifies three categories of learning.

Complexity

Analytics consist of varying degrees of complexity, which provide richer, deeper insights as higher levels of complexity are attained. 

Watershed Method of Learning Analytics Complexity

Level 1: Measurement

The most basic form of analytics is measurement—the simple act of tracking things and recording values. 

Level 2: Data Evaluation

Evaluation is the process of trying to make meaning out of the data you’ve measured. And does the data mean something good or bad?

Level 3: Advanced Evaluation

When datasets get large enough,* you can use advanced evaluation techniques—such as exploratory and inferential analytics—to search for relationships within your data and discover powerful insights.

Level 4: Predictive & Prescriptive Analytics

The first three levels of the Watershed Method help you understand what’s already happened. The fourth level of this method covers predictive and prescriptive analytics, which help you understand what will happen in the future.

Category

When analyzing learning and training in the corporate space, organizations typically look at one of three possible areas:

Watershed Method of Learning Analytics Categories.

Category 1: Learning Experiences

Learning experience analytics seek to understand more about specific learning activities.

Category 2: Learner

Learner analytics seek to understand more about a specific person or group of people engaged in activities where learning is one of the outputs.

Category 3: Learning Program

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). 

Up Next: A closer look at complexity

Now that you understand how complexity and category factor into learning analytics, it's time to take a closer look at the four levels complexity. Join us next week as we explore these levels, and don't forget to sign up to have the latest installments of our series delivered straight to your inbox. 

 * The size of a particular dataset will vary depending on what’s being evaluated.


The Watershed Method

Download this helpful guide that illustrates how complexity and category come together to form the Watershed Method™ for learning analytics.

New Call-to-action


Mike Rustici

About The Author

As an innovative software developer turned entrepreneur, Mike Rustici has been defining the eLearning industry for nearly 20 years. After co-founding Rustici Software in 2002, Mike helped guide the first draft of xAPI and invented the concept of a Learning Record Store (LRS). In 2013, he delivered on the promise of xAPI with the creation of Watershed, the flagship LRS that bridges the gap between training and performance.