Learning Engineering @ Scale

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.

Call for Participation

Scaled learning requires a novel set of practices on the part of professionals developing and delivering systems of scaled learning. Learning engineering is 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 (IEEE ICICLE). This event will bring together learning engineering experts and scaled learning experts 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 conditions. 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.

Organizers

• Erin Czerwinski, Carnegie Mellon University
• Jim Goodell, Quality Information Partners
• Robert Sottilare, Soar Tech
• Ellen Wagner, University of Central Florida

Registration

To register for this workshop, please select this workshop when registering for Learning @ Scale 2020.