ADL collaborates with external vendors and academic institutions through our Broad Agency Announcement (BAA). BAAs are the U.S. Government’s competitive solicitation procedure to contract for basic and applied research and development. ADL uses our BAA to contract for Advanced Technology Development (budget activity 6.3).
FY17 Focused Areas of Research
ADL conducts research in six focus areas (e-learning, m-learning, learning theory, web-based virtual worlds and simulations, learning analytics and performance modeling, and interoperability infrastructure). ADL is always seeking good research ideas related to these areas; however, based upon requirements collection and strategic guidance, we may identify particular Topics of Interest periodically. In other words, we are especially interested in R&D projects that help meet these topics of interest (listed below) but remain open to other good ideas, so long as they fall within our mission. For FY17 (and to inform the ADL FY17 Broad Agency Announcement), our topics of interest are below.
ADL Focus Area: Learning analytics and performance modeling
FY17 Topic of Interest 1: xAPI integration with simulation, teams
Problem: xAPI enables fine-grained tracking of learner performance, but to date, developers have primarily exercised its utility for tracking individuals (versus teams) and generally outside of simulation-based learning environments. The challenge is to extend xAPI for multi-person settings and (individual or multi-person) simulations, develop associated learning analytics (ideally ones that are reusable and not bespoke to a given case study), and demonstrate the utility of the approach through empirical testing.
Possible approaches: Offerors may attempt to address this entire topic or may address key pieces of it. Examples of relevant pieces include: how to properly represent multi-person (e.g., team) activity in a multiplayer simulation, how to extend xAPI implementation to better address simulations and virtual environments, how to integrate xAPI with traditional distributed simulation interoperability protocols (e.g., HLA, DIS), how to best define generic xAPI profiles and associated learning analytics for simulation-based learning, and how to negotiate individual and team performance xAPI capture.
FY17 Topic of Interest 2: Persistent Learning Profiles for Lifelong Learner Data
Problem: Individuals’ training, education, and performance data tends to be trapped within data silos. xAPI enables data interoperability, but additional work is needed to reference, access, create, and generally manage integrated, lifelong learner profiles. Research is needed to address, for example, the types of data required in learner profiles (beyond competency frameworks), the technical specifications for efficiently and safely storing/retrieving these large datasets, questions related to data ownership, and technical mechanisms to verify trustworthiness and accreditation of data.
Possible approaches: Offerors may attempt to address this entire topic or may address key pieces of it. Examples of relevant pieces include: Investigating the technical and/or policy requirements for learner profiles, their data schemas and implementation specifications; defining the requirements and best practices for managing and sharing massive stores of learner information across a largescale infrastructure, such as the DoD or Federal Government; and exploring possible methods for verifying data, such as the use of blockchains to provide secure, permanent, and historical ledgers.
ADL Focus Area: Interoperability infrastructure (Total Learning Architecture)
FY17 Topic of Interest 3: Implementing and Testing xAPI Profiles
Problem: xAPI has been widely adopted and implemented in commercial applications, but to date, military implementation has been hampered by restrictive operating environments and inertia in infrastructure. Real-world xAPI military prototypes have been demonstrated as standalone proofs of concept—but not authenticated in true military network environments. Working examples and guidelines are needed to identify and help overcome barriers to implementing xAPI successfully in real-world military contexts.
Possible approaches: Offerors may attempt to address this entire topic or may address key pieces of it. Proposed projects should focus on identifying and overcoming the technological and bureaucratic barriers to practically implementing xAPI in DoD. Examples of possible project topics include the following: Demonstrate the deployment of xAPI profiles (e.g., SCORM, cmi5, video) in real military environments by addressing implementation barriers such as CAC authentication and IA/Cybersecurity restrictions; document cost-efficient infrastructure upgrade requirements needed to scale xAPI implementation beyond pilot prototypes; create practical resources (e.g., guides, authoring tools) for instructional designers to help them effectively use the expanded performance capture capabilities xAPI offers; and create generalizable software solutions that enable practical querying of raw xAPI data and synthetization of it in meaningful ways (e.g., dashboards).
FY17 Topic of Interest 4: TLA Ontologies for Semantic Interoperability
Problem: Distributed learning environments require both syntactic interoperability (the ability of two or more applications or agents to exchange information) and semantic interoperability (meaningfully interpreting exchanged data). Currently, TLA components (including xAPI) do not suitably support semantic interoperability. Therefore, we risk inadvertently creating closed applications, data silos, and semantic integration problems. Ontologies, reasoning systems, and vocabulary metadata solutions could make TLA data and components more expressive and compatible, but key questions remain.
Possible approaches: Offerors may attempt to address this entire topic or may address key pieces of it. Examples of relevant pieces include: proposing semantic interoperability best practices for standards, interoperability tools, and implementation approaches for the TLA; comprehensively reviewing and adapting appropriate existing standards for the TLA; and modeling specific components of the TLA using existing ontology or semantic web standards (e.g., RDF, OWL, Schema.org). Additional approaches for TLA semantic interoperability (other than those listed above) may also be submitted for this topic. The key question that must be addressed by any submissions is: What is the best semantic interoperability strategy for integrating TLA (e.g., xAPI) components?
FY17 Topic of Interest 5: TLA Infrastructure Security
Problem: The TLA is envisioned to be a framework comprised of multiple specifications and APIs (including xAPI) that enable robust interoperability across a persistent learning ecosystem. This environment necessarily includes extensive capture, recording, and sharing of potentially sensitive data, and serious security challenges must be addressed.
Possible approaches: Offerors may attempt to address this entire topic or may address key pieces of it. Projects proposed to this topic should help comprehensively identify cybersecurity concerns or requirements for TLA (xAPI) use in DoD and/or Federal Government, and/or provide recommendations for how to best address security requirements. For instance: How can the TLA (including xAPI) integrate sufficient protections without creating barriers to use? How can the TLA (or even just xAPI or its profiles) be practical and realistically supported in DoD and Federal Government while meeting all laws and official guidance, such as the Family Educational Rights and Privacy Act (FERPA) and DoD Instruction 8500.01 (Cybersecurity).
ADL Focus Area: Any of ADL’s Six Focus Areas
FY17 Topic of Interest 6: Other Innovations
ADL may also consider other submission topics if the proposal involves novel capabilities having promise to provide significant innovation in distributed learning contexts.