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Recommendation Across Many Learning Systems to Optimize Teaching and Training

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Advances in Human Factors in Cybersecurity (AHFE 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 782))

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Abstract

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 cyber operations, with their career development and growth and the acceleration of their expertise attainment.

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Acknowledgements

This material is supported by the Advanced Distributed Learning (ADL) Initiative under Contract Number W911QY-16-C-0019. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the official views of the U.S. Government or Department of Defense.

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Authors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Kelly J. Neville & Jeremiah T. Folsom-Kovarik

Authors
  1. Kelly J. Neville
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  2. Jeremiah T. Folsom-Kovarik
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Correspondence to Kelly J. Neville .

Editor information

Editors and Affiliations

  1. University of Central Florida, Orlando, Florida, USA

    Tareq Z. Ahram

  2. Soar Technology Inc., Orlando, Florida, USA

    Denise Nicholson

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Neville, K.J., Folsom-Kovarik, J.T. (2019). Recommendation Across Many Learning Systems to Optimize Teaching and Training. In: Ahram, T., Nicholson, D. (eds) Advances in Human Factors in Cybersecurity. AHFE 2018. Advances in Intelligent Systems and Computing, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-319-94782-2_21

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