Why Engineering is Needed for Scale

Why Engineering is Needed for Scale

This essay originally was presented at the Learning @ Scale workshop. It also is slated for inclusion in the forthcoming book, "Learning Engineering Toolkit," for which Jim Goodell is the lead author. For a primer on learning engineering, read this post by Jim Goodell What is Learning Engineering?.

World War II penicillin promotional poster
A poster used to promote penicillin during World War II

In 1928, Scottish scientist Alexander Fleming discovered Penicillin. The discovery was a scientific breakthrough that saved countless lives during World War II and since. The 1945 Nobel Prize in Physiology or Medicine was awarded to Fleming and two Oxford University scientists who made progress in making concentrated penicillin and proved effects of the drug. However, the Nobel Prize-winning scientists never developed the ability to produce the drug at scale. The Oxford team found that it was impossible to produce in sufficient quantities in its laboratory.

In 1941, mounting deaths from World War II battle wounds prompted the British scientists to reach out for help from the United States. The Rockefeller Foundation in New York arranged for Howard Florey and Norman G. Heatley to travel in July 1941 to meet with Charles Thorn, the U.S. Department of Agriculture's chief mycologist. As a result of that trip, 39 separate drug laboratories began producing penicillin. The United States entered the war later that year when the Japanese bombed the American fleet in Pearl Harbor, Hawaii.

But by June 1942, there was enough penicillin available to treat only 10 patients[1]. The urgency of lives lost in the war meant that production of penicillin needed to move out of the laboratory and into mass-production. This was no longer just a scientific endeavor; it required engineering.

The goals of science and engineering are different. The goal of science is to discover the truth about the world as it is. The goal of engineering is to create scalable solutions to problems using science as one tool in that endeavor. Like engineers, scientists use data to find the truth. They are concerned about reliability of the data and what the data reveals about general principles by which we understand our universe. Outlier data often is ignored as noise. Engineers are more concerned about outlier data. For example, scientists may be interested in better understanding the physics of flight. Engineers want to know what outlier conditions cause one specific airplane design to crash and for what reason. Scalable designs require manufacturing tolerances for parts that eliminate outlier failures.

Margaret Hutchinson Rousseau
Chemical and process engineer Margaret Hutchinson Rousseau

Chemical and process engineer Margaret Hutchinson Rousseau and her multidisciplinary team developed a deep-tank fermentation plant that enabled large-scale production of penicillin. By May 1943, deep-tank fermentation produced 400 million units of penicillin[2]. In July 1943, the United States War Production Board supported a plan for mass distribution of penicillin stocks to Allied troops fighting in Europe. Hutchinson’s engineered process and processes developed by other engineers, such as G. Raymond Rettew[3], made it possible for the United States to produce 2.3 million doses in time for the invasion of Normandy in spring 1944 and over 646 billion units per year by June 1945[4].

This story is a victory for both science and engineering.

Learning at scale also requires both science and engineering. Learning scientists discover “what works” in human learning, and learning engineering teams develop production models for scaled impact. Learning engineering aims to optimize a specific learning solution, to understand under what conditions and with what learners a current design is not optimal, and then develop and test alternative solutions toward optimized and scalable solutions.

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.


[2] Madhavan, G. (2015). Applied Minds: How Engineers Think. United Kingdom: W. W. Norton.



Jim Goodell

Jim Goodell (@jgoodell2) is Senior Analyst at QIP. He works on connections between education sciences, policy, practice, and personalized/optimized learning. Learn more about Jim here.

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