What follows is an early draft of the introduction to the Technical Report on xAPI being written by the IEEE LTSC Technical Advisory Group on xAPI. In the spirit of openness that this work is based upon and in our desire for public input, we offer both the text below as well as this link to the shared doc where you may leave comments and suggestions to the draft itself. We thank you for your interest and effort and on behalf of all of the 10 subcommittees who have contributed to and who continue to draft this work, I express my appreciation.
-- Shelly Blake-Plock, Chair
The Experience API, more commonly called xAPI, is an application programming interface which allows software applications to exchange data regarding human activity. Designed for the domain of learning, xAPI provides a means of allowing the capture of data on human performance as well as the pedagogical or experiential context of that performance.
Based around a simple “actor : verb : object” semantic structure, xAPI allows applications to share human and machine readable activity streams of data. Each individual semantic statement is referred to as a Learning Record. Clients that produce Learning Records are called Learning Record Providers (LRP). A server responsible for receiving, storing, and providing access to these Learning Records is called a Learning Record Store (LRS). And a client that accesses data from a Learning Record Store for the purpose of processing the data is called a Learning Record Consumer (LRC).
The open source xAPI data specification provides a means for technical implementers to capture data in a common semantic format whether from established elearning technologies such as learning management systems (LMS), learning content management systems (LCMS), and mobile learning applications and survey tools, or from emerging elearning technologies including digital and hybrid simulations featuring augmented reality and Internet of Things (IoT) devices and sensors, serious games and virtual reality; micro-certification providers and credentialing engines; technologies applied to novel human performance tracking contexts such as wearables and biometric devices; or on-the-job technologies and web services used in the course of business such as sales and marketing applications.
In real-world contexts, xAPI provides a means to track learner behaviors at a granular level. Examples of the types of activities which may be tracked include:
- interacting with an eBook or other web content including video
- communication between learners and mentors via chat, messaging apps, and social media
- digital experience and decision making processes within a videogame or simulation or within a VR headset
- test-taking behaviors including approaches to problem solving and puzzle-solving strategies; the duration of activities within assessment challenges; habits of skipping, fast-forwarding, muting, or reviewing video library content during an assessment; and raw performance outcomes
- cyber-physical interactions and team choreography in sensor-enabled learning environments
- activities within customer relationship management (CRM) applications which may be correlated against the outcomes of activities and instruction delivered via an elearning application to determine the effectiveness of training on compliance, efficiency, and performance
As a result, in the words of the Advanced Distributed Learning Initiative (ADL), “xAPI aims to facilitate the documentation and communication of learning experiences” in almost any performance assessment situation where there is a digital footprint.
The ADL Initiative is a United States federal government program reporting to the Deputy Assistant Secretary of Defense for Force Education and Training (DASD (FE&T)), under the Office of the Assistant Secretary of Defense for Readiness in the U.S. Department of Defense. ADL led the development of the xAPI specification as well as the associated xAPI Profiles specification, the xAPI LRS test suites, and other related technologies. xAPI is an open source project and is maintained at https://github.com/adlnet. As of November 2017, the xAPI specification is in version 1.0.3.
As an open source project, xAPI has been very successful in bringing together a wide community of developers, researchers, and business users across industry, academia, and government. In September of 2017, in an effort to leverage the collected wisdom of this open source community for the purpose of developing a community-driven guide to the technical implementation of xAPI, the IEEE Learning Technology Standards Committee (LTSC) voted to start up a Technical Advisory Group for xAPI (TAGxAPI).
In creating this guide to technical implementation, the members of TAGxAPI have worked to provide a document which would be valuable to an audience comprised of information technologists, data scientists, instructional designers and others needing technical knowledge in order to implement xAPI in the real-world.
The guide is organized as follows.
- Part One: We provide context via an example use case.
- Part Two: We discuss the technical understanding and implementation strategy necessary to deploy xAPI technologies to deliver our use case.
- Part Three: We provide a deeper dive into the technical implementation including how to leverage the xAPI Profiles specification.
- Part Four: We provide a “need to know” inventory around certification and other issues technical implementers should understand.
Additionally, the guide provides direction on where to access and how to contribute to a growing collection and variety of use cases and cases studies curated by TAGxAPI members. It is our intent to have provided a technical report worthy of its subject and worth your time as a technical implementer.
Note: We are estimating that the full document will be ready for publication near the end of December 2017. Join the official TAGxAPI mailing list to keep up with our progress.