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The Advanced Distributed Learning Initiative


The ADL Initiative strives to develop the next-generation of distributed learning science techniques and technologies via research, development, and collaboration. A sample of the ADL Initiative’s R&D projects is listed below, with similar projects grouped into portfolio areas (although, in practice, these areas often overlap and interconnect!). All projects are carried out by the ADL Initiative staff, external vendors, and/or scientists and engineers from Federally Funded Research and Development Centers (FFRDCs).


Distributed Learning Interoperability

Historically, the ADL Initiative has emphasized interoperability. Beginning with SCORM® in the mid-1990s and including xAPI today, the program invests heavily in ensuring that distributed learning systems can effectively exchange and make use of learning-related content and data. Current projects in this portfolio area roughly fall into the following subareas:

Total Learning Architecture

The Total Learning Architecture (TLA) is a set of internet and software specifications being developed to enable next-generation personalized, data-driven, and lifelong technology-enabled learning. Once complete, the TLA will include technical guidelines, such as Application Programming Interfaces (APIs) and data model descriptions, that define how training, education, and personnel management technologies “talk” to each other—both syntactically and semantically. The TLA will also define software services that perform whole-system processes using automation and artificial intelligence (AI). The TLA is not a particular training device or educational tool. Rather, it is the “glue” that connects all other learning technologies into an integrated, coherent system.

Learning Activity Providers

“Learning activity providers” are the learner-facing components in a distributed learning system. Traditionally, these have included e-learning pages and instructional videos. Today, mobile learning, web-based simulations, and e-books have also become more common. As part of the TLA effort, the ADL Initiative is exploring how contemporary and emerging learning devices can be effectively integrated into a learner-centric system-of-systems, advantageous share data, and—both individually and collectively— create unique and valuable learning experiences.


Learning Analytics and Data Visualizations

Improved measurement, storage, analysis, and remediation of learners’ performance is the lynchpin to the ADL Initiative’s future learning vision. Data-driven learning enables real-time adaptations, whether in an instructional or operational context, and will enable organizational adaptability at higher levels. In a world where learning is constant, data in the form of measurements and evaluations will be more pervasive and must be woven into the learning experience. To mature the idea of data-driven learning, the ADL Initiative is conducting projects in these subareas:

Competencies and Credentialing

In this context, “competencies” refer to formally-defined, organized, and structured descriptions of knowledge, skills, attributes, and other characteristics, and “credentials” refer to formally-asserted qualifications, such as college degrees or professional certifications. The ADL Initiative is exploring best practices—from learning technology and science perspectives—for defining, sharing, authenticating, and managing competencies and credentials.

Learner Experience Data

The ADL Initiative’s xAPI software specification enables interoperability of (human) performance data across widely varying training, education, and personnel management systems. This popular specification is becoming widely used throughout consumer products and is recommended for use across the Defense community in the Department of Defense Instruction (DoDI) 1322.26. Ongoing R&D projects involving xAPI, in particular, and learning experience data, in general, include the following:


Learning Science

Effective technology-enabled learning isn’t possible without the intentional application of learning science (e.g., pedagogical and andragogical best practices). Effective application of learning science can enhance any and all aspects of the previously outlined vision, and to be clear, the use of iterative, evidence-based learning science methodologies is a critical enabler of those elements.


Other Projects

Sometimes efforts don’t fit nicely into one of the categories listed above. Below are a few other ADL Initiative projects that help inform the future of distributed learning in unique ways.