I teach undergraduate and graduate level courses in mechanical engineering at a research oriented university in the United States. I am a teaching track professor and my sole responsibility, on which I am judged for pay raises or promotions, is teaching efficacy.
The courses I teach have a strong mathematical component to them. Some notable examples would be: computational fluid dynamics or finite element methods. For those here familiar with these courses, you would know that calculus background is necessary to understand the nuances.
However, I find that the students I teach this to (mostly 4th year undergraduate students) balk at the prospect of doing some analytical "pen-on-paper" work and would like only to know how to use relevant software skills for these, with no mathematical background.
This leads me to receiving unsatisfactory end of semester evaluations (3.90/5.00) from students with a lot of them complaining that "the course is too math-y"(verbatim). The department requires teaching professors to consistently receive a 4+/5.00 for courses they teach and base promotions, continuing contracts and raises on this.
Unfortunately, I refuse to pander and just teach students "software-button-clicking" alone. Software skills can be learnt from Youtube and they don't really need me to teach them that. I continuously tell them that mathematical/fundamental analytical skills are far more important because the software landscape is quite fickle and ever-changing based on industry whims and fancies. However, fundamental mathematical skills are resolute and robust and may be applied to most engineering problems.
Has this been encountered by other folks on this SE and is there some effective method you have devised to counter this? My university is significantly "engine research" driven and students end up getting jobs at the "big 4" automotive companies. Perhaps I need to ply my trade at a different department like "applied mathematics"?
On my part, I juxtapose software results with analytical results continuously to explain how analytical results are necessary for validating software results. However, students are under the impression that the "software is always right" irrespective of whether or not their problem set-up and boundary conditions are correct or not.