About the Advanced Distributed Learning (ADL) InitiativeThe Advanced Distributed Learning (ADL) Initiative is the Department of Defense research and development epicenter for learning science and technologies. Established in 1999, ADL serves as the thought-leader for facilitating learning for our nation’s Warfighters, government agencies, and beyond. Through research, development and collaboration, ADL is working on next generation strategies, best practices, and innovations to advance the way we learn.We are people like you -- innovators, teachers, leaders, researchers, developers, partners -- in search of a better way to deliver learning.Department of DefenseDoD_5e8dcfdc-5d6a-11df-839d-400e7a64ea2aWarfightersGovernment AgenciesInnovatorsInnovators Welcome: please join us on our research and development journey as we inspire the next generation learner.TeachersLeadersResearchersDevelopersADL LaboratoriesThe ADL Initiative operates in its network of laboratories in Alexandria, Virginia, and Orlando, Florida; in partnership with their collaborative labs (co-labs): the Academic ADL Co-Lab at the University of Wisconsin and the ADL Center for Intelligent Tutoring Systems Research & Development at the University of Memphis in Tennessee. The goals and capabilities of the ADL Initiative extend internationally via relationships with 10 ADL Partnership Labs in Canada, Latin America and Caribbean Regions, NATO ACT (in Virginia), New Zealand, Norway, Poland, Romania, Serbia, South Korea, and the United Kingdom.ADL Co-Lab NetworkThe ADL Initiative sponsors a network of collaborative-laboratories (Co-Labs). The ADL Co-Labs collaborate with government, industry, and academia to develop and disseminate common guidelines, research and initiatives, and tutorials for ADL; and to share resources among all the stakeholders. The ADL Initiative directly funds two of the Co-Labs: ADL Co-Lab in Alexandria, VA and ADL Co-Lab in Orlando, FL.ADL Co-Lab Alexandria, VirginiaLocated just outside Washington, DC, the ADL Co-Lab Alexandria serves as a clearinghouse across organizational boundaries to coordinate and lead the systematic development and refinement of the future learning environment. This ADL Co-Lab operates to stimulate development of technologies that enhance learning and performance across the Department of Defense (DoD) and other Federal agencies.ADL Co-Lab Orlando, FloridaThe ADL Co-Lab Orlando is located in Central Florida. Its mission is to enable the DoD Components’ training and education communities and acquisition programs to realize the ADL vision. The ADL Co-Lab Orlando serves as the ADL Initiative’s organization for adopting and implementing ADL across DoD Component organizations. It operates under the direction of the Deputy Assistant Secretary of Defense (Readiness).Academic Co-Lab Madison, WisconsinThe Academic Advanced Distributed Learning Co-Laboratory (AADLC) is an applied research and development center working in the field of online learning and digital media. Since spring 2010, The AADLC has been housed within the University of Wisconsin-Extension’s division of Continuing Education Outreach and E-Learning (CEOEL) where it performs work with both internal and external partners. The Co-Lab’s mission is to increase educational attainment and improve learning experiences through digital media research and development.ADL Center for Intelligent Tutoring Systems Research & Developmentat the University of Memphis in Tennessee.ADL PartnersThe goals and capabilities of the ADL Initiative extend internationally via relationships with 10 ADL Partnership Labs in Canada, Latin America and Caribbean Regions, NATO ACT (in Virginia), New Zealand, Norway, Poland, Romania, Serbia, South Korea, and the United Kingdom.CanadaLatin AmericaCaribbean RegionsNATO ACT(in Virginia)New ZealandNorwayPolandRomaniaSerbiaSouth KoreaUnited Kingdom... a better way to deliver learning._091a0a1c-9fad-11e5-b158-3763a90c73caTo serve as the thought-leader for facilitating learning for our nation's Warfighters, government agencies, and beyond._091a0b84-9fad-11e5-b158-3763a90c73caOpen SourceADL's mission is to develop and advocate open source software, tools, and specifications; as well as create and accelerate the establishment and utilization of technology-driven learning standards that prepare and enable our nation’s learners, both military and civilian.LearningStandardsTechnologyInnovative learning technologies impact our learners by enabling them to be more mentally agile, expanding their capacity to develop sophisticated competencies and skills, and enabling them to thrive in the most complex situations.AgilityCompetencySkillResearchCollaborationResearchInspire the next generation learner._091a0c88-9fad-11e5-b158-3763a90c73ca1InnovatorsInnovators Welcome: please join us on our research and development journey as we inspire the next generation learner.LeanersADL’s Learning Science and Technology (LS&T) research is learner-centric and tech-enabled, focusing on human dimension, data-driven learning, assessments, and social computing and social learning. The intent of ADL’s Research and Development (R&D) is twofold. One, to improve the ability of individual learners to thrive in the complex environment in which we work; and, second, to drive learning organizations to the art of the possible. ADL serves as the thought-leader for the Department of Defense (DoD) and other government agencies for learning science and learning technologies, enabling innovation, finding efficiencies, guiding customers into the future, and creating a shared vision and strategy for the larger community to collectively pursue. In order to achieve LS&T goals, ADL strives to ensure that LS&T innovations are clearly communicated to the community while partnering with DoD (and other government agencies), academia, and industry to collaboratively support LS&T. Further, ADL will facilitate transition, acceptance, and adoption of new LS&T via policy, communication, transition support, collection of empirical evidence, and enabling interoperability. Transition is ameliorated through open source development and open access to software, tools, specifications, and standards to influence broad adoption. ADL’s path to achieve LS&T success includes working with partners to shape requirements, collaborate on a clear strategy and vision for LS&T, and conducting internal (i.e., developing and prototyping the art of the possible) and external R&D (i.e., through Broad Agency Announcement performers).Individual LearnersImprove the ability of individual learners to thrive in the complex environment in which we work._091a0ddc-9fad-11e5-b158-3763a90c73ca1.1LearnersLearning OrganizationsDrive learning organizations to the art of the possible._c62d85d8-a085-11e5-bd3c-cf57a90c73ca1.2Learning OrganizationsProjectsDevelop projects._091a12e6-9fad-11e5-b158-3763a90c73ca2DesignersDevelopersADL is actively developing projects with forward thinking partners, inspired designers, and masterful developers.HPITProvide added capabilities to intelligent tutoring systems._091a13ae-9fad-11e5-b158-3763a90c73ca2.1Hyper-Personalized Intelligent Tutor (HPIT) is an open source system that provides added capabilities to existing intelligent tutoring systems. Most intelligent tutoring systems are able to detect a learner’s cognitive ability or knowledge on a subject and use that information to alter course of action and feedback. HPIT takes it a step further and is able to detect non-cognitive factors (e.g., determination, boredom, motivation) in a learner via plug-in applications, and manages the communication of that information between various tutoring systems. HPIT communicates with two iPad-based intelligent math applications to incorporate adaptive cognitive and personalized non-cognitive factors. Data collected from pilot studies of approximately 200 students using these math applications found that non-cognitive factors such as grit, learning orientation, and self-efficacy are targets for personalization (e.g., feedback, hint generation, starting task level, timing of tasks) that result in increased learning.SAVEProvide a framework for learning procedural skills through simulation._c62d8be6-a085-11e5-bd3c-cf57a90c73ca2.2Semantically Enabled Automated Assessment in Virtual Environments (SAVE) provides a framework for learning procedural skills (e.g., repairing a car, flying an airplane, or shooting/maintaining a weapon system) through simulation. The system observes the learner operating in an instrumented virtual environment, tracks and logs their activity, and compares logged learner activity to a Gold Standard. The comparison generates immediate feedback for the student and provides them with contextually-relevant hints and links to training materials. The comparison also provides the instructor with insight into student comprehension. In addition, the system features content authoring tools that instructional developers can use to develop training for the virtual environment.Mars GameEngage high school students, effectively teach and assess their critical thinking, math, and programming skills, while also introducing them to possible career opportunities in the Science, Technology, Engineering, and Mathematics field._c62d8e5c-a085-11e5-bd3c-cf57a90c73ca2.3High School StudentsThe Mars Game is a web-based game built on the virtual world framework. The Mars Rover has crash landed and the student must help the rover repair itself, build shelter, and prepare for colonists before they arrive. This game is designed to engage high school students, effectively teach and assess their critical thinking, math, and programming skills, while also introducing them to possible career opportunities in the Science, Technology, Engineering, and Mathematics field.DECALSSupport discovery, sharing, reusing, and repurposing of learning resources._c62d901e-a085-11e5-bd3c-cf57a90c73ca2.4The Data for Enabling Content in Adaptive Learning Systems (DECALS) project was a follow-on effort to integrate with and add capabilities to the Re-Usability Support System for E-Learning (RUSSEL) and the Learning Registry (LR). By combining RUSSEL, the LR, and the Experience API (xAPI), DECALS makes learning resources discoverable. It supports sharing, reusing, and repurposing learning resources. In addition, DECALS can be integrated into Personal Assistants for Learning (PAL) systems, which enable maintenance of information regarding an individual’s skill-set, knowledge, and competencies and offers suggestions for content based on those competencies. The DECALS application serves as a web-based search interface for the LR and provides the means for users to discover, store, and register resources in the LR. DECALS allows for integration: PAL and other learning systems can use DECALS functionality to help manage and curate resources. It also permits third-party services, like competency alignment services, to be added to it. DECALS maintains information on competencies that individuals have or want to have. It also has the ability to tag learning content with a description that can be interpreted by PAL systems to suggest learning content for an individual.Learning RegistryCapture, connect, and share data regarding learning resources online._c62d91fe-a085-11e5-bd3c-cf57a90c73ca2.5U.S. Department of EducationEducatorsStudentsThe Learning Registry (LR) is a new approach to capturing, connecting, and sharing data regarding learning resources online with the goal of making it easier for educators and students to access the rich content available in our ever-expanding digital universe. There is an abundance of learning resources online, but it can be time-consuming and difficult to discover credible, quality resources. The LR increases openness, sharing, and use of digital learning resources and supports learning organizations from across all education sectors. Importantly, the LR is not a specific destination, portal, or engine that educators will “go to.” Rather, it is an open source technology framework to which any learning content providers (e.g., teachers, publishers, and curriculum developers) are able to share their content. Think of the LR as an online highway or a network of roads that brings content to educators at the home pages they are already using to find resources—sites that need only to tap into the system. The benefit is that teachers and instructors can discover, use, reuse, and/or repurpose high-quality content. Further, the LR offers users a means to provide specific comments about which content they use, how they use it, and the efficacy of the content because it was rated by peers and other trusted sources. The LR is open source and serves as a repository of learning resources. Educators are able to search within the LR by Subject or Standard. They can review and choose the most relevant and highly rated resource to present to their students. Teachers, content developers, and vendors can add value to the LR by creating, refining, and submitting content. The LR is a joint project between the U.S. Department of Education and Department of Defense’s ADL Initiative.RUSSELFacilitate and promote e-learning content reuse._c62d93a2-a085-11e5-bd3c-cf57a90c73ca2.6The Re-Usability Support System for E-Learning (RUSSEL) was developed to facilitate and promote e-learning content reuse. Many barriers existed that limited reuse of content: content was not stored in approved and accessible repositories; the process of creating, registering, uploading, and maintaining content was time consuming; and there were concerns over intellectual property rights. The RUSSEL project attempts to address these barriers. RUSSEL is a web-based open source software solution with a complete digital library/repository of e-learning content. It has built-in integration with the Learning Registry (LR) and offers additional functionality. Users are able to search and discover objects and manage and repurpose courses, documents, and multimedia assets. The e-learning content in the RUSSEL system includes ratings, analytics, and comments. These features allow users to find and use the best content available. In addition, RUSSEL improves the design and development of training. It includes an Electronic Performance Support System (EPSS) that helps users create reusable Sharable Content Object Reference Model (SCORM)-conformant content. It supports all development environments and styles with tools that take users through the process of identifying objectives, selecting an instructional design strategy, and adding content to a template associated with that strategy.3D RepositoryShare, find, organize, and download 3D models._c62d9550-a085-11e5-bd3c-cf57a90c73ca2.7Sergeant First Class Paul Ray Smith Simulation and Training Technology CenterNaval Postgraduate SchoolNational Aeronautics and Space Administration Ames Research CenterLockheed MartinL3 TechnologiesThe 3D Repository (3DR) is a software platform for sharing, finding, organizing, and downloading 3D models. Development on the 3DR began because finding quality, free 3D content in a useable format was difficult, developing new 3D content was costly and time consuming, and tools for organizing collections of content were unavailable. 3D models within the 3DR are simple artistic models. Anyone is able to contribute 3D Models to the Repository through a fast, interactive, web-based tool. Key contributors typically are ADL, Sergeant First Class Paul Ray Smith Simulation and Training Technology Center, the Naval Postgraduate School, the National Aeronautics and Space Administration Ames Research Center, Lockheed Martin, and L3 Technologies. Contributors control access to their content, but ADL supports creative commons licenses (i.e., open access). The 3DR also serves as a simple way to search for, find, download, and reuse 3D content. Ratings and reviews on models are available to inform decisions. File format conversion makes the process of downloading and using content easy. The 3DR is open source software, which allows for users to install and manage inventories of 3D models locally, enables integration with other repositories and applications (e.g., Virtual World Sandbox), and permits other organizations to use for creating commercial software.PERLSEnable learners to have increased control over what, when, and how they learn._c62d971c-a085-11e5-bd3c-cf57a90c73ca2.8LearnersImagine a mobile application that can distinguish between whether you are studying to get your Project Management Professional certification and that you are currently in the waiting room for a doctor’s appointment. Based on that information, it suggests reading a short article on the best ways to prepare for the final test. PERvasive Learning Systems (PERLS) is a personal learning system mobile application currently being developed to support the Personal Assistant for Learning vision. It enables learners to have increased control over what, when, and how they learn and takes into account that finding relevant content can be time-consuming and challenging. PERLS is capable of monitoring a user’s GPS, interests, expertise, schedule, media preferences, and daily routines. It uses this information to suggest learning content and persuades the learner to read that content based on their personal preferences and goals, time and location constraints, and level of expertise in the form of Smart Lists and Smart Alerts. There are a selection of different Smart Lists a user can browse through: Quick Picks when a user has limited time to consume content; Surprise Me when a user has no time constraints; To Do when a user is trying to make progress towards an established goal; and Here and Now when a user needs to see content related to a place or location. Smart Alerts, on the other hand, are suggestions that generate automatically based on needs, context, and preferences.PALMsMake it simple to create learning content that takes advantage of both perceptual and adaptive pedagogical methods._c62d98e8-a085-11e5-bd3c-cf57a90c73ca2.9The Perceptual Adaptive Learning Modules (PALMs) is a rapid authoring tool that makes it simple to create learning content that takes advantage of both perceptual and adaptive pedagogical methods. Perceptual learning involves repeatedly exposing a learner to specific visual information. This authoring tool was used to make a flash card application that facilitates mastering a subject through pattern recognition. Feedback is given to a learner after every answer and after periods of longer trials to show trends. In addition, adaptive sequencing is used. This means that if a learner answered an item incorrectly, that item would be presented again shortly after. If a learner answered the item correctly, that item would not appear again for a longer period of time. A student’s level of mastery is determined based on accuracy and response time. Once a learner masters an item or subject area, it is removed from the flash card application. Currently, integration efforts with the PERvasive Learning System mobile application are underway. The team is also interested in collaboration efforts with organizations that have content for use or are open to field testing the application and system.Mobile Pilot ProjectDeliver a free mobile application that provides access to unclassified, releasable mobile JKO training and performance aiding content._c62d9aa0-a085-11e5-bd3c-cf57a90c73ca2.10The Joint Knowledge Online (JKO) / Advanced Distributed Learning (ADL) Mobile Pilot Project is a collaborative effort between JKO and the ADL and its partnership lab network to deliver a free mobile application (App) that provides access to unclassified, releasable mobile JKO training and performance aiding content. The App is available for both Apple iOS and Android smartphones. About JKO -- JKO is the DoD-sponsored, Joint Staff training portal providing 24/7 global, distributed learning access to Web-based joint courses, special area curriculums, and simulation training applications. Training is administered, tracked and reported by the JKO Learning Content Management System (LCMS). To learn more about JKO, visit the splash page at http://jko.jten.mil.MoTIFExplore new types of learning and design approaches that take advantage of the capabilities of the mobile platform._c62d9c6c-a085-11e5-bd3c-cf57a90c73ca2.11Mobile learning is a new educational technology and introduces both exciting capabilities and complexity into the learning design process, but with very few guidelines. ADL’s MoTIF project will explore new types of learning and design approaches that take advantage of the capabilities of the mobile platform. The MoTIF project will result in interventions such as strategies, materials, products, and guidelines as solutions to the problems, but will also advance our knowledge about the characteristics of these interventions and the processes involved in designing and developing them. What is the problem (gap) this project is aimed at solving or the objective it is trying to accomplish? * supporting alternative learning methods (e.g., spaced learning, performance support); * leveraging the capabilities of the mobile platform (e.g., camera, sensors, GPS) They are simply shrinking distributed learning courses down to fit the smaller screen sizes of smartphones and tablets. As a growing number of mobile innovations become available in the learning space, education and training technology thought leaders are also interested in how to effectively design for a variety of mobile learning scenarios beyond self-paced training courses.Alternative Learning MethodsSupport alternative learning methods (e.g., spaced learning, performance support)_c62d9e2e-a085-11e5-bd3c-cf57a90c73ca2.11.1Mobile PlatformsLeverage the capabilities of the mobile platform (e.g., camera, sensors, GPS)_c62da54a-a085-11e5-bd3c-cf57a90c73ca2.11.2TAP AppPrepare Service Members for transition out of the military by providing guidance to assist in the process._c62da7de-a085-11e5-bd3c-cf57a90c73ca2.12Service MembersThe TAP App is a research prototype mobile application that was built with the Transition Assistance Program (TAP) and their Goals, Plans, Success (GPS) curriculum in mind. Those programs are geared to help prepare Service Members for transition out of the military by providing guidance to assist in the process. The transition into civilian life can be a challenge because circumstances differ and the process can be a long one where one has to coordinate many steps over the course of up to a year, or quite disorienting due to a short transition period that starts while deployed across the globe or shortly upon returning home. The TAP App explored using the power of smartphones to assist users in both managing and making the transition as smooth as possible, while also assisting in maximizing the potential for success in civilian life. Although the project was an exploration of supporting the transition scenario, it was also a means for ADL to explore implementation of xAPI for both recording of user / learner experiences and adaptive content that adjusts to a user’s individual circumstances and personal preferences. There is not yet an official association with the TAP or GPS.Mobile Learning Decision PathHelp instructional designers plan and implement mobile performance support._c62da7df-a085-11e5-bd3c-cf57a90c73ca2.13Instructional DesignersAdayana, Inc.Adayana, Inc. created this guide for the Advanced Distributed Learning (ADL) Initiative to help instructional designers plan and implement mobile performance support and to repurpose existing learning content for the mobile platform. Combating Terrorism Technical Support OfficeThe DoD’s Combating Terrorism Technical Support Office (CTTSO) funded the project.The Mobile Learning Decision Path (MLDP) is a new tool for stakeholders who are considering a mobile learning development project... Design requirements for desktop e-learning applications are different than the design requirements for mobile learning applications. Too often, these differences have been oversimplified or glossed over -- reducing “design” to merely forcing desktop content to a smaller screen. The result is a poor user experience and lost training or performance support opportunities. The MLDP is a PDF document structured as a decision support job aid, with questions and branching to further questions and to best practices -- depending on the answers provided at each step. Two sample projects illustrate how an instructional designer could approach a project using the decision path presented in the MLDP. One example assumes a conversion scenario, from desktop e-learning to mobile learning. The other assumes a performance support scenario, a fast-growing area for mobile learning solutions.Firesmoke Pocket GuideAssist fire practitioners, policymakers, regulators, and citizens with issues surrounding prescribed fire use._c62da7e0-a085-11e5-bd3c-cf57a90c73ca2.14Coalition of Prescribed Fire CouncilsThe Coalition of Prescribed Fire Councils partners with Councils across the country to "create one voice to assist fire practitioners, policymakers, regulators, and citizens with issues surrounding prescribed fire use." The Coalition is also active in facilitating the development of Councils in those states that do not yet have one.SERPPASSERPPAS is a unique six-state partnership comprised of state and federal agencies that promotes collaboration in making resource-use decisions supporting conservation of natural resources, working lands, and national defense.Fire PractitionersThis pocket guide was created by the Coalition of Prescribed Fire Councils and SERPPAS... The guide is a list of rules, checklists, and other resources to serve as a job aid for those needing to recall the rules and guidelines associated with proper prescribed smoke management practices and planned burns.MASLOAllow content delivery to multiple mobile platforms._c62dac98-a085-11e5-bd3c-cf57a90c73ca2.15Mobile devices have become commonplace tools in society (Kennedy, Smith, Wells, & Wellman, 2008) but learning systems have not adequately shifted to take advantage of the new platforms. Existing systems were created to support desktop, browser-based types of interaction with consistent network support. Mobile devices have smaller screens, and unpredictable network access, but these devices are nearly always with individuals creating the potential for training and access to materials when they are needed, in a dramatically more learner-relevant manner (e.g. Squire, 2009). The Academic ADL Co-Lab has developed a platform in partnership with the Florida Virtual School (FLVS) for delivering content packages through the Apple iPhone®. This platform is currently limited to delivering test preparations for high school students. The existing system also includes a method to store content in a commercially available massively distributed computing environment (i.e. “cloud computing”). Additionally, the current platform has a basic editing tool allowing teachers or instructional designers the ability to edit or create content. Research Summary -- The platform that we propose creates a new level of content delivery and tracking by expanding the capabilities of the system built for FLVS. This new system would allow content delivery to multiple mobile platforms and include greater content development flexibility for the U. S. Department of Defense training and performance support needs. The project has just kicked off an is in the second month of the first year. This section will grow as advancements are made. Deliverables There are three deliverables for this project: * An open source code base that can be used and modified. Parts will include a content editing tool built in Adobe AIR®, database definitions for storage and retrieval, and two mobile platform shells (Apple iOS4 SDK and Android SDK). * A functional prototype demonstration of the code components listed above. * Documentation of the functional specifications, implementation descriptions, and recommendations for future development. Current ideas for future development are other mobile platform support, mobile SCORM needs, and LMS requirements.TIP Mobile CourseConduct research on the conversion of a desktop course to enable delivery on a mobile device and to determine the issues and level of user satisfaction._c62dac99-a085-11e5-bd3c-cf57a90c73ca2.16The first thing many think of when mobile learning is mentioned is "e-Learning Lite." We believe that mobile offers so many more capabilities and opportunities and encourage trainers to “think outside the course.” However, in some cases short courses do work on mobile devices. The ADL mobile learning team was tasked with doing applied research on the conversion of a desktop course to enable delivery on a mobile device and to determine the issues and level of user satisfaction. We started with a survey of DoD Mobile Landscapes and have written a paper on our findings that will soon be available. We knew that Merrill Lynch had a successful course conversion of several of their compliance courses from desktop to mobile in 2007 and have talked extensively with them. Thus we started looking at DoD compliance courses. ADL had been involved with the initial creation of the Trafficking in Persons awareness course through Academic ADL Co-Lab since 2005. Trafficking in Persons (TIP) is a general awareness course mandated by DoD Instruction 2200.01 Combating Trafficking in Persons. ADL was provided access to a new, updated version. Linda Dixon, Program Manager, DoD CTIP Law Enforcement Policy and Support, OUSD(P&R) DHRA was supportive of making the awareness course content available on mobile devices to increase the access of the problem of human trafficking. We have continued to work with her through this process. Research Summary -- The conversion of content from desktop (HTML, Flash and two videos) to a mobile version with no Flash was completed in about a week and lessons learned were captured. In May and June 2011 the mobile version was deployed as a research project to test on additional devices and to get user feedback. We also conducted in-person focus groups. Jason Haag will be presenting a paper on these findings at I/ITSEC, 28 November – 1 December. Several vendors with tools for such a conversion were very interested, but we were not comfortable using any single vendor’s tool. We did offer them access to the content for one of the six modules if they wanted to put together a sample using their options. To date, thirteen vendors have converted the content and we have all learned so much more about potential user experiences and options seeing the multiple approached to the same content. The team has given several presentations on this experience at which the attendees also expressed that they had not thought of so many variations. Both the recent ADL presentation and the combined vendor presentations are available. We also created an EPUB version to be used with separate assessments and tested a SCORM version on several LMSs.Learning Record StoreStore, access, and visualize data about learning experiences, activities, and performance._c62dac9a-a085-11e5-bd3c-cf57a90c73ca2.17A Learning Record Store (LRS) is the implementation of the server-side requirements associated with the xAPI specification. The LRS is a key component of the xAPI architecture. It is the application interface for storing, accessing, and often visualizing the data about learning experiences, activities, and performance. An LRS is also required to validate the format of the statements as many of the requirements in the xAPI specification are targeted toward the LRS component. An LRS could be optionally integrated with any application such as a LMS, Human Resources (HR) system, or it could serve as centralized data store in a enterprise learning ecosystem. Third party applications which send or retrieve learning activity data will interact with the LRS as the data store for xAPI data. An LRS could also simply provide value as a stand-alone application without any integration points. As part of our research on xAPI, ADL developed an open-source LRS intended to provide a platform for testing and prototype purposes only. It should not be relied upon for scalability or security like a commercial LRS application should. The ADL LRS can be used in conjunction with many of the Open Source tools from ADL as well as others from the xAPI community.REAPERCollect data from live firing ranges using the Experience API (xAPI) and the associated Learning Record Store (LRS). _c62dad6a-a085-11e5-bd3c-cf57a90c73ca2.18U.S. ArmyU.S. Marine CorpsFederal Law Enforcement Training Center (FLETC)Department of Homeland SecurityFederal Bureau of InvestigationRange Experience Acquisition Portal for Evaluation & Reporting (REAPER) is a great success story involving collecting data from live firing ranges using the Experience API (xAPI) and the associated Learning Record Store (LRS). The main takeaways are that Corrective Interactive Multimedia Instruction (IMI) can be delivered to the shooter, total range performance for the day can be delivered to the coach/commander, and range operations data can be delivered to the maintenance personnel. The ability to receive individualized feedback; analyze group performance to make informed decisions; and identify favored lanes and targets, target life cycle data, and required maintenance were not possible before. The process begins with the shooter entering a 4-digit code into the student station and completing training. While the training is occurring, xAPI statements are transmitted to the Range LRS. The Range LRS transmits data to the Riptide LRS, which is connected to the reporting database where the data is aggregated to develop the Corrective IMI. Following the training, the shooter, coach/commander, and researcher are able to log in to their respective portals to view the results. The initial proof of principle took place at the U.S. Army Oscar 9 Range in Ft. Benning, GA, but the system and data analysis that resulted can be used by a number of other services and agencies, including U.S. Marine Corps, Special Forces, Federal Law Enforcement Training Center (FLETC), Department of Homeland Security, and the Federal Bureau of Investigation.MathCraftBuild a Personalized Assistant for Learning that will increase a student's knowledge by acting as a peer who needs help learning math._c62daf68-a085-11e5-bd3c-cf57a90c73ca2.19Math StudentsThe objective of MathCraft is to build a Personalized Assistant for Learning that will increase a student’s knowledge by acting as a peer who needs help learning math. It has evolved into a 3D Science-Fiction Adventure Video Game designed specifically to reinforce 6th grade math and science concepts. The student can often overcome an in-game challenge by helping the main character, an avatar named Elle, model a math problem. The game is built on the artificial intelligence system, Cyc, which watches and learns from the student’s actions and makes changes to Elle’s mental models based on those actions. Students are therefore able to spend more time focusing on teaching Elle areas where they have the most difficulty. A dashboard monitors class, individual progress, and performance so teachers can easily target areas of the curriculum that need emphasis. Currently, schools in Texas are piloting MathCraft, and efforts are underway for a more significant pilot in a German DOD Educational Activity middle school, where it will undergo rigorous testing.Open Social Learner ModelIncrease student motivation and performance._c62db24c-a085-11e5-bd3c-cf57a90c73ca2.20StudentsThe Open Social Learner Model (OSLM) leverages the power of open social learning modeling and adaptive navigation support in the context of the envisioned Personal Assistant for Learning system. OSLM is designed to increase motivation and performance in students that use the system by providing them with access to the Mastery Grids user interface. This interface includes visualizations that display progress of a student’s knowledge by topic and compare personal progress with the progress of the class. It also offers direct access to the learning content. Currently, OSLM is being used as supplemental material at the University of Pittsburgh, Winston-Salem State University, and National Sun Yat-Sen University in Taiwain. In addition, several universities were targeted with larger class sizes. On the development side, progress is being made to add learning content and authoring tools, implement cross-content sequencing (i.e., how to guide students to content of different types), and develop a new infrastructure for data collection.2015-12-10http://adlnet.gov/about-adl/OwenAmburOwen.Ambur@verizon.net