Besides my usual semesters of computer science courses and research, this fall I'm cross-enrolled at a neighboring university that offers education classes. Last night had some very interesting conversations. We are starting to prepare syllabi for the course we'd either ideally teach or want to be prepared to teach. I was in a group with two education PhDs (most of the class are). They consented to consider an introductory computer science course and answer two questions.
What are common naive theories that students have entering the course?
How might the course be designed to encourage students to reconstruct their theories?
So what naive theories do students have?
First, computers are magical. No, computers do exactly what a programmer tells them to do. (More advanced students learn about race conditions, compiler influence on correctness, etc). Which, unfortunately, means that if a computer is not doing what you want it to do, then you instructed it incorrectly (c.f., The rat is always right).
Second, I'm going to be a game programmer. No, most computer scientists do not write games (or at least, aren't paid to). But we find many other interesting parts to the field. Besides, many game programmers are treated little better than grad students.
Do you know other naive theories?
Then after class, I spent some time discussing more "advanced" theories in computer science.
Functional versus imperative programming. Does one paradigm exist to rule them all? Is one class of programming languages sufficient? Do students gain by learning about both paradigms? I discussed this briefly in Is versus ought, and have been regularly reading a strong function view in Existential Type.
Big 'O' notation and algorithm / data structure selection. I previously discussed this some in Know your N. And was co-author on a paper, "Brainy: effective selection of data structures", that demonstrated actual data structure selection for a program is not always best from the "Big 'O'" point of view.
Language equivalence. Related to functional versus imperative and one of my first posts, "Problem Solving via Programming", programming languages are theoretically equivalent (i.e., turning complete). But in practice languages should be selected for particular problems. What problems are best for specific languages?
What are some other major theories about computer science that students should know?
2 comments:
A topic that seems to be a difficult one to place in a collegiate curriculum is database systems. There seem to be few very good introduction and survey courses for various database concepts. I have seen good graduate level theory courses.
This was something I saw with new grads getting out into the workplace and writing absolutely horrible database designs even though they've 'had a database class.' Data persistence systems are getting a lot of focus from the market these days as things 'move to the cloud.' I think many people (not just early CS students) are a bit naive about the problems faced with databases and data organization, especially as the problem sets scale into the 'big data' realm.
I have to admit that I know nothing about database design, having never taken "a database class". I only understand them from the OS perspective, which states that a database uses all of the resources and tries to manage them itself.
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