A sequel to “The Reason Engineering School Let You Down”
The last article I wrote for this newsletter elicited a lot of responses. It is the most read and commented upon article in the series thus far. That tells me that many of us have a deep interest in the education and training of engineers. It also revealed something else – many seemed to assume that if you do well on the exam, then you understand the respective theory.
I am going to explore that assumption more in this article: Do good exam marks really mean good understanding? It’s all on the surface. When I was an academic and involved in education research, I was introduced to a phenomenon called surface learning. It is where students study to pass the exam as opposed to studying for understanding. We all probably have some experience doing this, where we drilled questions (maybe even used Schaum’s) or we remembered things. Or we know a fellow student who would get good marks, but never seemed to actually understand anything. That’s all surface learning. You can get away with this when the exam is set such to allow for this. And many exams are like that. They don’t assess understanding, just your ability to drill problems to pick up on the procedure and repeat it at speed. As an example of how pervasive this is, take the time to watch the video below. It features Eric Mazur, a physics lecturer, talking about his students and how shocked he was when he assessed conceptual (read “actual”) understanding after the first year of physics.
Some key points from the video:
Surface learning isn’t just about the student it is encouraged by most exams in engineering courses around the world. As I mentioned in the previous article: many exam questions will list only the variables needed to find the answer. In such a scenario, students only need to recognise the pattern (or the recipe). This is why grades don’t always (and often don’t) reflect understanding. The exam format can encourage procedural fluency at the cost not conceptual understanding. But what to do about it now? If you would like to improve your conceptual understanding of first principles, and you should, then one of the best sites you can go to is Arbor Scientific. They offer numerous teaching resources that you can sign up for, but they also have a great conceptual questions page - https://www.arborsci.com/pages/next-time-questions. Go check them out and get the resources once you are done. I liked the double boiler question and the bikes and bee question. See which ones get you thinking or reveal your lack of understanding so you can improve it. Before I finish though, I’d like to ask: what conceptual understanding tools do you know of? I am always keen for more and others here can benefit from them too.
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You’ve probably already thought about the answer to that question. I hope you at least believe that one of these three is essential—rather than relying solely on instinct or heuristics for all your engineering decisions. Most, however, haven’t thought about each of the three in enough detail to fully grasp the implications and limitations of each approach. Ideally, at this point, you’re thinking about first principles. Indeed, each of these three represents a different way of applying first principles. So let’s consider how you can best use them for your first principles.
ExperimentationIt’s hard to argue with reality. And that’s what experimentation offers. If the experiment fails, it doesn’t matter if your calculations or simulations say it should work. Experimental outcomes are the ultimate judge. The issue with experimentation from an engineering perspective is that it always "works"—even if you aren’t aware of what’s important. You can’t choose to ignore or suppress key variables. They’re always present and always have a value, whether you’ve thought about them or not. You might set all the key variables you think are important, but there are still others you’ve set inadvertently—because reality has already given them values. That means you might believe you’ve experimentally found a solution to your problem, but when you implement it, an issue arises. Why? Because you were unaware of a key variable—and its value during implementation differs enough from what it was during your experimentation to cause a failure. Experimentation won’t alert you to your ignorance of key variables until it’s too late. An example I mentioned in my book involves an engineer designing a device to control water flow for watering plants. Their experimentally developed design worked, but once the system was implemented, variations in temperature—and thus viscosity—rendered it useless. The engineer had conducted all the experiments at roughly the same temperature. Since they didn’t realize how temperature-sensitive viscosity is, they didn’t factor it into their tests—and reality had silently set that variable for them. CalculationCalculations have the advantage of forcing you to account for all key variables. The formulae you use have been developed after considerable attention by experts who have identified the important variables at play. If the engineer in the earlier example had taken the time to read up on the theory and find the appropriate formula, they would have learned how critical viscosity is. Then, while looking up viscosity values to put in the formula, they would have seen how much viscosity changes with temperature. Formulae also reveal opportunities for optimization. You can see which variables are raised to a higher power and thus offer more "bang for your buck." You can also work out whether variables should be increased or decreased to maximize your output—which isn’t always obvious. Sometimes, you can even deduce if an optimum point exists. However, there aren’t always formulae available for your exact situation. Consider again the water control device: what if it had an outlet orifice that was non-standard, and the engineer couldn’t find a discharge coefficient for it to plug into the formula? Experimentation could be an answer—but it might be time-consuming if multiple variants had to be fabricated and tested. SimulationObviously the newest of the three, simulation is almost like a mix of the other two. Simulation can come very close to reality—assuming it’s a well-developed system—and it can force you to specify all variable values, forcing you to note all those at play. However, some simulation systems “help” you by asking you to specify a material instead of individual material properties. Thus, you might find yourself back in the same situation: unaware of all the important variables, and unaware of which ones are best to adjust for optimization. Also, simulations still rely on limiting assumptions. Often, we must simplify systems for the sake of usability. So your simulation might not be a perfect representation of what you’re actually working on. While simulation can offer tremendous benefits when it comes to testing ideas and improving systems, it’s still not a silver bullet. What to Do Then?It’s likely clear to you by now that you need to use all three—experimentation, calculation, and simulation—if you want the insights, speed, and confirmation needed to find the optimum solution. Beware of any engineer who suggests you should focus on one and ignore the others. |
AuthorClint Steele is an expert in how engineering skills are influenced by your background and how you can enhance them once you understand yourself. He has written a book on the - The Global Engineer - and this blog delves further into the topic. Archives
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