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Recommendation across Many Learning Systems to Optimize Teaching and Training

2018; AHFE, Applied Human Factors and Ergonomics; Neville, K.J.; Folsom-Kovarik, J.T.

To help learners navigate the multitude of learning resources soon to become available in the Total Learning Architecture (TLA) ecosystem, a Recommender algorithm will give learners learning resource recommendations. Recommendations will support immediate training needs and provide guidance throughout one's career. This paper describes initial work to define the logic that will be used by the Recommender. It describes our use of (1) expertise acquisition theory and (2) research on the learning effects of learner state and characteristics. The descriptions are accompanied by examples of relevant research and theory, the learner-support guidelines they suggest, and ways to translate the guidelines into Recommender logic. The TLA, together with the Recommender, have significant potential to aid professionals across a range of complex work domains, such as cy ber operations, with their career development and growth and the acceleration of their expertise attainment.

Contract: W911QY-16-C-0019

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