I've been interested in John Sweller and Cognitive Load Theory since reading Ray Lister's ACE keynote paper from a couple year's back. I assigned several papers on the topic (see the papers in the References) to my educational technology class. Those papers have been influencing my thinking about how we teach computing.
In general, we teach computing by asking students to engage in the activity of professionals in the field: by programming. We lecture to them and have them study texts, of course, but most of the learning is expected to occur through the practice of programming. We teach programming by having students program.
The original 1985 Sweller and Cooper paper on worked examples had five studies with similar set-ups. There are two groups of students, each of which is shown two worked-out algebra problems. Our experimental group then gets eight more algebra problems, completely worked out. Our control group solves those eight more problems. As you might imagine, the control group takes five times as long to complete the eight problems than the experiment group takes to simply read them. Both groups then get new problems to solve. The experimental group solves the problems in half the time and with fewer errors than the control group. Not problem-solving leads to better problem-solving skills than those doing problem-solving. That's when Educational Psychologists began to question the idea that we should best teach problem-solving by having students solve problems.
The paper by Kirschner, Sweller, and Clark (KSC) is the most outspoken and most interesting of the papers in this thread of research. Their title states their basic premise: "Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching." What exactly is minimal instruction? And are they really describing us? I think this quote describes how we work in computing education pretty well:
There seem to be two main assumptions underlying instructional programs using minimal guidance. First they challenge students to solve “authentic” problems or acquire complex knowledge in information-rich settings based on the assumption that having learners construct their own solutions leads to the most effective learning experience. Second, they appear to assume that knowledge can best be acquired through experience based on the procedures of the discipline (i.e., seeing the pedagogic content of the learning experience as identical to the methods and processes or epistemology of the discipline being studied; Kirschner, 1992).
That seems to reflect our practice, paraphrasing as, "people should learn to program by constructing program from the basic information on the language, and they should do it in the same way that experts do it." The paper then goes on to present all the evidence showing that this "minimally-guided instruction" does not work.
After a half-century of advocacy associated with instruction using minimal guidance, it appears that there is no body of research supporting the technique. In so far as there is any evidence from controlled studies, it almost uniformly supports direct, strong instructional guidance rather than constructivist-based minimal guidance during the instruction of novice to intermediate learners.
There have been rebuttals to this article. What's striking about these rebuttals is that they basically say, "But not problem-based and inquiry-based learning! Those are actually guided, scaffolded forms of instruction." What's striking is that no one challenges KSC on the basic premise, that putting introductory students in the position of discovering information for themselves is a bad idea! In general, the Educational Psychology community (from the papers I've been reading) says that expecting students to program as a way of learning programming is an ineffective way to teach.
What should we do instead? That's a big, open question. Pete Pirolli and Mimi Recker have explored the methods of worked examples and cognitive load theory in programming, and found that they work pretty well. Lots of options are being explored in this literature, from using tools like intelligent tutors to focusing on program "completion" problems (van Merrienboer and Krammer in 1987 got great results using completion rather than program generation).
This literature is not saying never program. Rather, it's a bad way to start. Students need the opportunity to gain knowledge first, before programming, just as with reading. Later, there is a expertise reversal effect, where the worked example effect disappears then reverses. Intermediate students do learn better with real programming, real problem-solving. There is a place for minimally guided student activity, including programming. It's just not at the beginning.
Overall, I find this literature unintuitive. It seems obvious to me that the way to learn to program is by programming. It seems obvious to me that real programming can be motivating. But KSC respond to this, too.
Why do outstanding scientists who demand rigorous proof for scientific assertions in their research continue to use and, indeed defend on the bias of intuition alone, teaching methods that are not the most effective?
This literature doesn't offer a lot of obvious answers for how to do computing education better. It does, however, provide strong evidence that what we're doing wrong, and offers pointers to how other disciplines have done it better. It's a challenge to us to question our practice.