Information technology advances will support advanced distributed learning anytime and anywhere. However, similar advances in learning technologies are required to achieve cost-effective readiness and enhanced job performance. Adaptive learning that accommodates mastery differences in individual learners also offers benefits of high media reuse for continuum training - initial, refresher, remedial, and just-in-time instruction and performance aiding. High media reuse also can accrue from multiple courses supporting curricula related by personnel, equipment, or domain/core skills.
The Office of Naval Research (ONR) sponsored a Dual-Use Applications Program (DUAP) through the Naval Air Warfare Center Training Systems Division (NAWCTSD) to further "Artificially Intelligent Tutoring for Advanced Distributed Learning." A competitive procurement resulted in a technical investment agreement with Asymetrix Learning Systems, Inc. and Sonalysts, Inc. to enhance existing technologies and commercialize the resulting product (s).
The technical approach creates and delivers an individualized education plan at run-time. The first level of adaptivity determines "what to teach" by selecting and ordering the presentation of topics (that correspond to learning objectives). Topics are selected based on course definition data consisting of instructional groupings (course, module, lesson, etc.), instruction and testing strategy, and prerequisites, as well as current learner mastery. The second level of adaptivity determines "how to teach" by selecting specific learning objects (that support specific objectives/topics) based on student characteristics, mastery, and instructional history. Learning objects are data files consisting of one or more frames and associated media references that are attributed with objective/topic, detail level, score-based criterion, learner population.
After reviewing instructional issues, the paper also addresses the mechanisms, processes, and lessons learned from the DUAP technical investment agreement including Government goals and objectives. In addition to user-community involvement and program management from NAWCTSD, representatives from the Office of the Secretary of Defense, and Defense Acquisition University participated in working groups to evaluate progress and interim products, and to consider changes in instructional design processes to exploit adaptive learning capabilities.
The paper concludes with the implications for linking of adaptive learning capabilities to simulation-based tutors, embedded performance support, and learning management systems. Specifically, the learner model architecture is compatible with several simulation-based tutors, objective-based scenario generation, and training evaluation tools that have been developed under NAWCTSD training research programs.