[bane]

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.

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.