If you get ahead early, you are likely to stay ahead, or even move further ahead. If you fall behind a bit towards the beginning, for whatever reason, you are likely to fall more and more behind in the following weeks and months. Therefore, programming courses are self-amplifying systems. Thus, if you don’t understand one section of the course, you will likely also struggle in the following sections, unless you spend extra time to catch up and make up for what you missed before. Every topic strongly builds on the topics previously covered. What is going on might be this: The material in programming courses is highly hierarchical. What I really want to talk about is an idea that I picked up from a fascinating seminar that Anthony Robins gave a few months ago in our department: That the cause of the high failure rate that we are observing might not be any kind of intrinsic capability, but caused by the sequential nature of the material we are teaching. It interprets the data in a way that the data just does not support. The first paper linked above, for example, draws what I believe to be severely invalid conclusions. Let’s say, I am at least highly sceptical.
Some people really believe in these predictors, some do not. This has led to a whole lot of research about programming ability predictors: ways in which we can predict, by looking at people’s prior activities, performance, social context, or any other aspect of their lives, or with a hopefully simple test, whether or not they will be successful in learning programming before we make them go through it. That there are people who are just good at it, and others how simply cannot get it. One conclusion has been that the ability to understand programming is somehow intrinsic. (And an ironic side note is that we as teachers tend to react to this by aiming our teaching at the medium level-thus teaching to a group that contains hardly any students at all…)įor programming teachers, this poses some interesting questions and challenges. It has been observed in programming courses all over the world, largely independent of geographical or social context, and over a long period of time: The same pattern that we observe now existed 10 years ago, and 20 years ago, and 30 years ago. The surprising thing about this distribution is how constant it seems to be.
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“In every introduction to programming course, 20% of the students just get it effortlessly - you could lock them in a dimly lit closet with a reference manual, and they’d still figure out how to program. Mark Guzdial, in a blog post, calls it the 20% Rule:
We have a whole bunch of students getting very good marks and a lot of them failing. This graph shows a typical distribution of final marks in a first programming course. Here is an example:įigure 1: The double hump distribution of marks in a programming course The double hump refers to the marks distribution in a typical introductory programming course.
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