New Profession or Transformational Process? — Campus Technology

Learning Engineering: New Profession or Transformational Process?

A Q&A with Ellen Wagner

“Learning is one of the most personal things that people do; engineering provides problem-solving methods to enable learning at scale. How do we resolve this paradox?“ —Ellen Wagner

Learning engineering is an emerging methodology that attempts to combine theories and practices from the learning sciences with problem-solving approaches from engineering, ultimately to create valid, reliable, and repeatable solutions that can improve learning at scale.

As such, is learning engineering the next phase of instructional design? Will instructional designers trade in their creative jobs to become “learning engineers”?

Many observers see learning engineering as a new professional field. Others, including some already working in the fields of instructional design and development aren’t so sure…

Ellen Wagner is a widely recognized technology strategist, innovator, and advisor to both industry and academia as a founding partner at North Coast EduVisory. Wagner, who notably developed the Predictive Analytics Reporting (PAR) Framework, has worked at the heart of some of the most transformational and influential teaching and learning movements. Here, she guides us in an exploration of learning engineering by reflecting on what she calls the “learning engineering paradox”: Can the highly personal activity of learning be transformed into quantifiable outcomes enabled by scaled learning solutions?

Mary Grush: What is learning engineering and what is the learning engineering paradox?

Ellen Wagner: Learning engineering is an emergent practice that seeks to combine the theoretical knowledge about human learning with pragmatic problem-solving methodologies from engineering. The hoped-for outcome is the development of empirically valid solutions for improving learning, at scale. As you can imagine, it is a topic of significant interest among innovation and academic transformation stakeholders.

Current interest in learning engineering has been driven in some measure by experts coming from scientific disciplines outside of education and the social sciences. The emergence of “big data”, predictive analytics, machine learning, neural networks, and now generative AI have underscored that social science and education research methods are essential but no longer sufficient to accommodate the kinds of research explorations that are possible using research methods from the hard sciences.

The emergence of “big data”, predictive analytics, machine learning, neural networks, and now generative AI have underscored that social science and education research methods are essential but no longer sufficient to accommodate the kinds of research explorations that are possible using research methods from the hard sciences.

Learning engineering presents us with a conceptual paradox: Learning is one of the most personal things that people do; engineering provides problem-solving methods to enable learning at scale. How do we resolve this paradox?

After more than 5 years of actively poking and probing at the construct of learning engineering, I am finding that the greatest value offered by learning engineering may come from thinking of it as a process for applying empirical evidence of learning efficacy: to turn learning evidence into action.

The greatest value offered by learning engineering may come from thinking of it as a process for applying empirical evidence of learning efficacy: to turn learning evidence into action.

It appears that this process of transformation is catalyzed by using design methods, from design practices. Learning engineering connects the results from learning science research to targeted interventions used with specific audiences in specific settings, applying research results in practice. Using iterative design techniques, learning designs are evaluated and improved on their way toward being scaled for broader use in a variety of practice settings. This is where engineering methods ensure technological reliability.