As adoption of xAPI takes hold, it allows for more robust and interesting tracking of the learning process. As actual performance and results data are integrated with learning metrics, we will have the data we need to tailor the learning process to individual needs, and can draw more useful conclusions about the learning as a whole across a wider population. This leaves instructional designers with a set of critical questions: How does this impact my work? What tools do I need? What data should I track? What infrastructure does my organization need to be able to take advantage of this?

Choosing a new learning platform is a little bit like falling down a rabbit hole. With more than 700 platforms from which to choose, getting lost is easy. Why not learn from the soul-sucking experience of those who have traveled this journey before? 

Good courseware engages the user with multiple elements. From video to audio to the venerable drag-and-drop, you need to make sure your content is doing the job. But is your audience actually learning anything? More importantly, are they learning the lessons you need them to? And if they are, do you have the tools to prove that to your leadership?

Are we really able to tell how people use digital learning? How do we gain visibility that learning is being applied? Sophisticated tracking gives us the ability to gain insights from user behavior. By focusing on what we can successfully capture—such as time spent within the learning, what learning assets are most popular, and even user sentiment—it's now possible to gain a rich picture of the experience of learning. In this talk we'll explain why tracking is more important than ever, show you technology innovations that push measurement beyond the LMS, and demonstrate how to infer meaning from the data being captured to inform decision-making.

xAPI enables us to track a wide array of data but shareholders are interested in results, not the technology. Simply tracking everything is not a strategy and often creates noise that makes xAPI data more difficult to use. Instead, you need to step back to identify WHAT needs to be tracked with xAPI, and only afterward go through the process to design quality statements.

As xAPI has gained increased adoption across the industry, challenges have emerged as we have seen inconsistencies in both the expression and structure of xAPI data generated from a wide variety of tools and sources. While the xAPI profile specification was designed to define and standardize the data structure, more support and definition is needed on how to apply xAPI profiles for specific use cases. In practice, many organizations are developing their own unique approaches without any consistency in xAPI usage that can be re-purposed for a larger audience.           

One in five newly-graduated registered nurses quits within the first year of their first job, never to return to nursing. Exit interviews often reveal the same basic stories: nurses feel unsupported and overwhelmed by the transition from school to nursing practice. But what if hospitals had an early warning system to inform interventions that address a newly-graduated nurse's concerns before a nurse feels like they have to quit? And how could this early warning system be used in other situations, as well?

Training professionals find it difficult to prove the ROI of their efforts. That makes it harder to grow programs, sell training services, or find our "seat at the table”. Many of us can't get past Kirkpatrick's levels one or two to measure our training outcomes, and as a result can't also measure the broader impact of our training efforts. How do we get access to the data we need, and how do we successfully use that data to tell a story that resonates with stakeholders?

What if there was a way to get more accurate data on what, how, and when people needed to learn? Standard L&D analytics generally measure content: Who took what course or module? Where did they take it? When? And, did they like it? We assess with ADDIE models, trying to ensure we hit the right pain points for learners. We use surveys and guesswork. Yet we ignore social learning, the very best source of learning data in an organization.