Your access to learning-related data has grown dramatically over recent years. But just because you have a large volume of data doesn’t mean it necessarily provides value. While tools like the xAPI make it increasingly easy to acquire data about learners’ activities, this information provides little benefit if you don’t know how to design to acquire meaningful data, interpret that data, or improve your learning design based on what you’ve discovered.

Every revolution requires both dreamers and pragmatists. The xAPI dreamers have been painting a beautiful picture of what is becoming possible. To realize its full potential, the pragmatists need to catch up. The xAPI needs a very specific set of work completed to be successful, and it needs organizations to participate in making the next generation of learning systems and impacts possible.

Good courseware will use multiple elements such as video, audio, interaction, and good old-fashioned reading. You struggle to balance all of it until it’s a finely harmonized symphony of information, waiting for a student to take it all in. But are any of those activities or videos you’ve worked on actually helping anyone learn? How can you show the relationship between the activities and performance?

Much has been made about the astounding dropout rates for massive open online courses (MOOCs): typically 85 percent or more. While it may be misleading to apply the traditional metrics of higher education to MOOCs, there are ways to dramatically improve the numbers.

When a new specification such as the xAPI comes out, it is important to understand the basics. And one of the most critical basics of the xAPI is the concept of statements: the way most xAPI data is communicated. Before getting started with an xAPI implementation, you’ll want to have a strong understanding of what statements are and how you can form them yourself.

Big data and learning analytics are top of mind for many organizations throughout the training industry. However, while there is a lot of of information about what you can do with data, there is much less about what you should do. Since data can include deeply personal or sensitive information, training organizations cannot consider using this information without first thinking about the ethics of what they are intending to do with that data and the privacy of individual learners.

You know that taking online courses isn’t the only way to learn. You want to invent new learning formats and experiences that better meet your audience’s needs. However, in many cases you need to include tracking, and the types of learning trackable by traditional learning management systems are limited. The Experience API (xAPI) specification is flexible enough to track a wide range of learning experiences, but it’s a new technology, and barriers to adoption still exist. How do you bridge this gap?

In the past few years, many organizations have introduced recognition and certification programs using digital badges, but too often the results produced by these programs have disappointed. Badge earners don’t bother to claim or share their badges, and as a result, issuing organizations have wasted their time and effort.

Technology like xAPI has provided the learning industry with the opportunity to track anything, anywhere, and L&D and HR leaders are hailing the arrival of people analytics. These advances provide substantially more data about the people in an organization, but what do you do with the data? Are you gathering the right information? Where do you even start?