When Learning Engineering Meets Instructional Design

Concurrent Session 8
Streamed Session

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Brief Abstract

The need for scientific competence related to education and training programs design, development and evaluation has existed for decades. As technology has continued its advances, engineering methods are increasingly valued for learning and development initiatives that depend upon data science, computer science and learning science to structure solutions and to measure outcomes and results. Learning engineering recognizes that the development of new tools and architectures to help advance learning can benefit from engineering expertise. This session explores what this may means to instructional design and instructional designers alike.

Presenters

Ellen Wagner is an award-winning learning technologist with experience in multiple industries, including post-secondary education. She is an ed tech industry entrepreneur, with three successful exits from companies where she was either a co-founder or director. She is a former doctoral research faculty member and higher educational academic affairs administrator. She successfully navigates market forces and conditions between and among these unique research and practice environments, through multiple waves of innovation and transformation. Today, as Founder and Managing Partner of North Coast EduVisory Services, LLC, she and her colleagues guide higher education and commercial clients in making better use of data analytics resources, talent, and tech platforms for long-range ed tech and L&D strategic planning. Ellen is also an Affiliate member of the Faculty of the College of Education at George Mason University. She is a member of the IEEE-Industry Connections Industry Consortium on Learning Engineering (ICICLE) Steering Committee. She serves on the editorial boards of the Journal of Computing in Higher Education, eLearn Magazine, and the Journal of Applied Instructional Design. She currently serves on the Board of Directors of two private start-up companies; she is a member of the Board of Directors of the AECT Foundation. She has held positions as tenured professor, department chair and academic affairs administrator (University of Northern Colorado); senior executive in publicly-traded and private commercial software companies (Informania, Inc; Viviance new education AG; Macromedia, Inc.; Adobe, Inc.; Hobsons); as a VP of technology for WICHE, the Western Interstate Commission for Higher Education and ED of WCET. While at WCET she co-founded the Predictive Analytics Reporting (PAR) Framework, a predictive analytics research effort that was successfully acquired by Hobsons in 2016. PAR is now part of the student success software product called Starfish Retention Solutions.

Extended Abstract

This session describe the emerging field of learning engineering, to show some of the different models of learning engineering that have emerged, and and to consider the many opportunities that learning engineering may offer to the ongoing professionalization of instructional design and development. We believe that the partnership between IDs and learning engineers is one of the essential first steps for integrating learning engineering methodologies into educational practice, and could think of no better place to bring our early work for review and consideration by ID peers.

The term "learning engineering" was coined 50 years ago by Herbert A. Simon while at Carnegie Mellon, who saw the essential benefits of applying technical competencies to learning design. Today, Simon's vision is evident as learning engineering emerges as both a professional practice and an academic discipline. Today's learning engineering is framed as the application of engineering methodologies in developing learning technologies and infrastructures to support learners and learning. We will explore the opportunities, challenges, and essential collaborations required to realize full benefits of this emerging discipline.

Session Outcomes: * Explore what learning engineering is, what it does, how it differs from ID and why they both matter * Contribute important educational perspectives to the value of creating a new discipline for driving greater value in learning tech * Discuss strategies for assembling effective "learning tech teams" including learning engineering and ID.