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Learning Engineering @ Scale

Published: 12 August 2020 Publication History

Abstract

Scaled learning requires a novel set of practices on the part of professionals developing and delivering systems of scaled learning. IEEE's Industry Connections Industry Consortium for Learning Engineering (ICICLE) defines learning engineering as "a process and practice that applies the learning sciences, using human-centered engineering design methodologies, and data-informed decision-making to support learners and their development." This event will bring together learning engineering experts and other interested conference participants to further define the discipline and strategies to establish learning engineering at scale. It will also serve as a gathering place for attendees with shared interests in learning engineering to build community around the advancement of learning engineering as a professional practice and academic field of study.
Interdisciplinary research in the learning, computer and data sciences fields continue to discover techniques for developing increasingly effective technology-mediated learning solutions. However, these applied sciences discoveries have been slow to translate into wide-scale practice. This workshop will bring together conference participants to give input into models for scaling the profession of learning engineering and wide-scale use of learning engineering process and practice models.

References

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Glowa, L. and Goodell, J. (2016) Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning Vienna, VA.: International Association for K-12 Online Learning (iNACOL).
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Goodell, J., Kessler, A., Kurzweil, D., Kolodner, J., (2020). Competencies of Learning Engineering Teams and Team Members. IEEE ICICLE 2019 Conference on Learning Engineering.
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Sottilare, R., Long, R., & Goldberg, B. (2017, April). Enhancing the Experience Application Program Interface (xAPI) to Improve Domain Competency Modeling for Adaptive Instruction. In Proceedings of the Learning @ Scale Conference, Cambridge, Massachusetts, April 20-21, 2017.
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L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale
August 2020
442 pages
ISBN:9781450379519
DOI:10.1145/3386527
© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2020

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Author Tags

  1. data-informed
  2. engineering design
  3. human-centered
  4. learning
  5. learning engineering
  6. learning sciences
  7. multidisciplinary teams

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Overall Acceptance Rate 117 of 440 submissions, 27%

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