Sometimes, you want to define relationships. Other times, you want to add context. When describing time, direction, location, and spatial relationships, we depend on prepositions. But that’s been a difficult task in the realm of xAPI.

Until now.

Meet xAPI Prepositions — a set of 150 machine-readable xAPI concepts delivered in a conformant xAPI Profile.

Every training event could be represented in a digital data profile.

The Experience API (xAPI) has transformed the landscape of learning technology, enabling the capture of diverse learning experiences across various platforms. However, designing xAPI Profiles has traditionally been a cumbersome and labor-intensive process.

One of the most interesting aspects of xAPI is the ability to model time.

For the last few years, we’ve gotten a steady stream of calls asking us to design xAPI Profiles for business needs. Eventually, we thought to ourselves — what if we just gave people the power to author xAPI Profiles themselves?

One of the most frustrating things about xAPI is that it is difficult to gauge how much an implementation is going to cost. In order to account for xAPI statement size, volume and throughput, and data design quality when pricing an xAPI solution, we recommend building a synthetic data set representative of the data that you expect to run through your system.

Whereas an xAPI data statement describes an action or activity, xAPI Profiles combine xAPI data statements to describe experiences. They are fundamental to unlocking the power of xAPI. And they are notoriously difficult to design. Fear not. We’ve designed a presentation to help you to understand how to design perfect xAPI Profiles.

In the same way that Learning Engineering itself is developing into a sort of bridge between these disciplines in the pursuit of scaling meaningful and measurable learning experiences, likewise, as a data broker between the worlds of synthetic training and distributed learning analytics, GIFT serves as a sort of AI-enabled technological bridge.

Within a research context, there is a need for large and relevant activity data sets to test hypotheses regarding competency assertion. But where do these data sets come from?

If xAPI is the way to describe and track learning activities, xAPI Profiles is the way to describe and track learning experiences. Check out this recorded webinar from the xAPI Cohort to learn all about the power of xAPI Profiles.

Yet Analytics has released two new open source libraries for xAPI Profile validation. The first is called Pan and is used to validate xAPI Profiles. The second is called Persephone and is used to validate xAPI statements against xAPI Profiles. Both libraries are distributed under the Apache 2.0 license.