Resolving the Learning Engineering Paradox with Science, Solutions and Scale


I’ve been thinking about learning engineering a lot these days. Learning engineering is a term used to describe a possible new field of professional endeavor that combines skills and competencies from learning sciences with those from engineering. Learning engineering purports to blend a rigorous understanding of the sciences describing how people learn with the systematic, solutions-oriented approaches of engineering to develop empirically validated, replicable learning solutions that can scale.

At the core of this statement lies a fundamental paradox: learning is one of the most personal, individualized activities in which humans engage. How do we align the constructs, theories, epistemologies and methodologies that validate and describe the deeply personal journey of learning with the methods, models, measures, instrumentation and standardization required to build valid, reliable engineering solutions that scale?

The question is important. Even though there has been much research activity from scholars from the learning sciences about the power that learning engineering affords, no single dominant model or approach has appeared, nor has a single “definition of the field” managed to engage and inspire a true “community of practice”. Instead, we have a number of communities of research and interest (e.g. Carnegie Mellon’s Open Learning approach, Schmidt Futures and the Learning Agency’s classroom experimentation and continuous formative improvement approach, and ICICLE’s “applied learning sciences” crowdsourcing approach, to name three examples). There is no agreement about the essential skills and competencies required to successfully execute learning engineering. Job descriptions for learning engineers still sound like instructional designers who use data in their work.

There might be a better way to do this. Perhaps talking about WHY people should care about learning engineering, and considering the benefits that learning engineering REALLY offers to learning and development professionals, partners and stakeholders might make this a less muddied conversation. With that orientation it may be easier to develop evidence-based frameworks to organize our thinking about the influences, the epistemologies, the methodologies and the relationships among connected constructs.

Some colleagues and I have been conducting some exploratory work with ontologies to see if we can determine if learning engineering truly represents a new profession, or if what we are seeing is something else. Using several commercial LLMs and a series of prompts we have created several ontology frameworks, using constructs from fields of expertise that contribute to its quintessential descriptors. The frameworks make it possible to deconstruct, contrast and compare various attributes of what scholars and practitioners say learning engineering is. Developing an ontology is an evidence-driven process for describing essential attributes of a discipline.

The ontological work is shifting my perspective of where the value of learning engineering may actually lie. Rather than being a new profession or a persona, it appears that learning engineering may actually be better conceptualized as a process, a process that transforms evidence of scientific validity and impact into designs to protype learning efficacy, on the way toward determining which solutions are worthy of scale.

I am personally excited that learning engineering might help solve one of the thorniest challenges that educational researchers and practitioners face: It is really hard to directly apply research into practice settings. Using the professional disciplines of the learning sciences, designs for learning enablement and the reliability and scalability of engineering, it may be possible to directly apply results of research studies using designs as the catalyst for learning enablement coming from the professions to target specific conditions and apply research finding to address specific conditions, audiences and performance expectations.

You are going to be reading more on this topic. Time to buckle up.