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I agree there is probably a quality difference in average teaching, but I didn't find much of a difference in the objective/subjective nature (I was a CS major with a philosophy minor). In the lower-division courses, both CS and most liberal arts have objective answers you can test on, many of which are memorization. Comparison-based sorting can't be done in better than O(n log n); Marx was not present at the Paris Commune (neither was Bakunin, though he was involved in a related uprising in Lyon); most hash-table designs have amortized constant time for most operations; a key difference between Fichte and Kant was that Fichte rejected the notion of a thing-in-itself; etc. I think it may be true that CS is more likely to actually test on them, in part because many universities expect their liberal-arts courses to double as de-facto "how to write" courses, so they're supposed to have students do essays rather than test them on the subject matter. Imo they would be better structured as having intro-level classes teach subject matter that's tested on in a more rigid style, and then move students to developing their own arguments in essays more towards the 201 level once they've gotten a certain basic amount of factual knowledge under their belt. But that would require teaching intro writing skills somewhere else, and the main alternative, a dedicated "freshman composition" type class, is pretty widely disliked by students and not considered a great success, because it's sort of disembodied writing, "how to write" without any actual subject matter. Once you got to upper-division courses, most of my philosophy and my CS classes were pretty similar in terms of not really having objective, right-or-wrong answers. There were definitely things you could do that were ridiculous and therefore wrong, but for the most part it involved making a case for something. A data-mining project, for example: Given a data set, what can you conclude from it? What's your evidence, what are the potential pitfalls with your evidence, how would you present the results? The methodology is different, but in terms of general approach and the subjectiveness of grading, that felt very similar to me to a philosophy course project, one of which was: develop and argue a case for or against the possibility that machines could produce "creative" output. In both those examples it has less to do with there really being a "right" answer than with being sufficiently fluent with the tools of the domain to build and coherently present a supportable case, while avoiding doing anything that's clearly "wrong", like misusing statistics or using examples that don't logically support your point. |
I actually think that supports my idea here -- that a competently trained engineer should be able to operate competently in a traditionally liberal arts field -- because a competently trained engineer has to learn both skill sets. Upper level courses like Data Mining require both the engineering bits, and the art bits of the liberal arts.
Interpreting the results of a Data Mining algorithm requires a similar critical analysis and writeup of any upper level Lit coursework. The demonstration of how the student wrote their analysis is as important as their conclusions.
This is different than say "write a paper detailing the differences and similarities between decision trees and neural networks", which is akin to "write a paper about the goals and purposes of the Paris Commune -- and why it failed".
The differences then between an engineer and a English major, in the field of English, is not a matter of skill, but a matter of exposure. An engineer is less likely to have studied in depth the brief life of the Paris Commune, but should be able to competently write about it once they know the material. What makes Engineering hard as a discipline is that the Engineer must not only have memorized the material, and be able to competently write about it, they must also apply it and show it working. That 3rd bit is why people drop from engineering programs and where the right/wrong objective evaluation brutalizes the unprepared student who was expecting subjective evaluation of their work.