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The Advanced Distributed Learning Initiative

R&D at the ADL Initiative

The ADL Initiative is a research and development-based organization with a commitment to advancing learning for the Federal government, defense partners, scholarly research community, and the learning industry as a whole. The advancement of learning is primarily performed through government-owned projects and external vendor-executed projects that the ADL Initiative oversees.

Approach

The ADL Initiative’s work begins with understanding the Federal government’s distributed learning needs. First, our stakeholders provide requirements (from end-users, developers, and operational leaders); we also collate existing requirements publications, crosswalking DoD and Government agency needs. (Download our 2017 DoD Distributed Learning Gaps report to see the result of part of this process.) Second, the ADL Initiative integrates strategic guidance from the White House, Congress, Defense Department, and the US Federal agencies and coalition military partners. (See “At the Tipping Point,” a recent article highlighting why and how defense and security leaders think training and education systems need to evolve.) Finally, we actively monitor emerging science and technology trends, scouting for new techniques or tools that can enhance distributed learning.


The ADL Initiative focuses on Budget Area 6.3 (Advanced Technology Development) Research, Development, Test, and Evaluation (RDT&E) to provide learning science, specifications, guidance and best practices, and technology applications to the DoD, Federal agencies, and coalition partners. This means that we take “low fidelity” laboratory prototypes and mature them into workable systems, ready for real-world “test flights.” To be effective, we must overcome the research-practice gap between good ideas working in a lab and functional systems that work under real-world conditions in order to bridge the so-called “valley of death” that so often defeats good research and development (R&D).


R&D has two mutually supporting lines of effort. Internal R&D directly creates science and technology deliverables, which are government-owned and vendor neutral. These efforts are complementary to external R&D—projects executed by external vendors via our Broad Agency Announcement (BAA), which are managed by the ADL Initiative engineers and researchers. These two lines of effort constantly inform one another, and, over time, several successful smaller-scale projects may grow into a broader, more comprehensive solution.

 

Areas of Focus

The ADL Initiative’s R&D projects contribute to the program’s mission to help modernize defense and government distributed learning. These efforts also inform associated distributed learning policies, guidelines, and technical specifications. Specific areas of focus include a diverse range of science and technology topics.


The ADL Initiative explores contemporary and emerging distributed learning tools. For instance, the program examines the development, delivery, optimization, and support of e-learning and mobile learning systems. This work encompasses the creation, delivery, and tracking of web-based interactive multimedia learning; development of best practices for e-learning policy and processes; and assessment of various supporting capabilities, such as Learning Management Systems (LMSs), content registries, Massive Open Online Courses (MOOCs), e-books, smartphone-based learning, and browser-based simulations, games, and virtual worlds (VWs).


The ADL Initiative also focuses on interoperability specifications for distributed learning systems. The program’s well-known efforts in SCORM® and xAPI fall into this line of effort. Similarly, other projects align with this theme, such as projects aimed at integrating disparate systems into modular and service-oriented architectures, securely sharing learning data across systems, and developing appropriate metadata and paradata for system-of-systems distributed learning offerings.


Another closely related area of focus involves “data-driven learning.” Projects in this area examine the best practices for collecting learner performance data, structuring it, safely and ethically storing it, sharing it across systems, conducting meaningful learning analytics, visualizing the data, and using those results for effective personalization of learning. This line of effort involves data science (“big data”), work on competencies, credentialing, learner profiles, data visualizations, open/social learner models, and associated privacy and information security concerns.


Lastly, the ADL Initiative explores associated learning science (e.g., pedagogy and andragogy), including the application and evaluation of theories of learning to primarily digital learning technologies. This research is pervasive, touching all aspects of R&D performed by the ADL Initiative.