Transfer of learning: Making sense of what education research is telling us
I enjoy reading “Gas station without pumps,” and the below-quoted post was one I wanted to respond to.
Two of the popular memes of education researchers, “transferability is an illusion” and “the growth mindset”, are almost in direct opposition, and I don’t know how to reconcile them.
One possibility is that few students actually attempt to learn the general problem-solving skills that math, CS, and engineering design are rich domains for. Most are content to learn one tiny skill at a time, in complete isolation from other skills and ideas. Students who are particularly good at memory work often choose this route, memorizing pages of trigonometric identities, for example, rather than learning how to derive them at need from a few basics. If students don’t make an attempt to learn transferable skills, then they probably won’t. This is roughly equivalent to claiming that most students have a fixed mindset with respect to transferable skills, and suggests that transferability is possible, even if it is not currently being learned.
Teaching and testing techniques are often designed to foster an isolation of ideas, focusing on one idea at a time to reduce student confusion. Unfortunately, transferable learning comes not from practice of ideas in isolation, but from learning to retrieve and combine ideas—from doing multi-step problems that are not scaffolded by the teacher.
The problem with “transferability” is that it’s an ill-defined term. Certainly, there is transfer of skill between domains. Sharon Carver showed a long time ago that she could teach debugging Logo programs, and students would transfer that debugging process to instructions on a map (mentioned in post here). That’s transferring a skill or a procedure. We probably do transfer big, high-level heuristics like “divide-and-conquer” or “isolate the problem.” One issue is whether we can teach them. John Sweller says that we can’t — we must learn them (it’s a necessary survival skill), but they’re learned from abstracting experience (see Neil Brown’s nice summary of Sweller’s SIGCSE keynote).
Whether we can teach them or not, what we do know is that higher-order thinking is built on lots of content knowledge. Novices are unlikely to transfer until they know a lot of stuff, a lot of examples, a lot of situations. For example, novice designers often have “design fixation.” They decide that the first thing they think of must be the right answer. We can insist that novice designers generate more designs, but they’re not going to generate more good designs until they know more designs. Transfer happens pretty easily when you know a lot of content and have seen a lot of situations, and you recognize that one situation is actually like another.
Everybody starts out learning one tiny skill at a time. If you know a lot of skills (maybe because you have lots of prior experience, maybe because you have thought about these skills a lot and have recognized the general principles), you can start chunking these skills and learning whole schema and higher-level skills. But you can’t do that until you know lots of skills. Students who want to learn one tiny skill at a time may actually need to still learn one tiny skill at a time. People abstract (e.g., able to derive a solution rather than memorize it) when they know enough content that it’s useful and possible for them to abstract over it. I completely agree that students have to try to abstract. They have to learn a lot of stuff, and then they have to be in a situation where it’s useful for them to abstract.
“Growth mindset” is a necessity for any of this to work. Students have to believe that content is worth knowing and that they can learn it. If students believe that content is useless, or that they just “don’t do math” or “am not a computer person” (both of which I’ve heard in just the last week), they are unlikely to learn content, they are unlikely to see patterns in it, and they are unlikely to abstract over it.
Kevin is probably right that we don’t teach problem solving in engineering or computing well. I blogged on this theme for CACM last month — laboratory experiments work better for a wider range students than classroom studies. Maybe we teach better in labs than in classrooms? The worked examples effect suggests that we may be asking students to problem solve too much. We should show students more completely worked out problems. As Sweller said at SIGCSE, we can’t expect students to solve novel problems. We have to expect students to match new problems to solutions that they have already seen. We do want students to solve problems, too, and not just review example solutions. Trafton and Reiser showed that these should be interleaved: Example, Problem, Example, Problem… (see this page for a summary of some of the worked examples research, including Trafton & Reiser).
When I used to do Engineering Education research, one of my largest projects was a complete flop. We had all this prior work showing the benefits of a particular collaborative learning technology and technique, then we took it into the engineering classroom and…poof! Nothing happened. In response, we started a project to figure out why it failed so badly. One of our findings was that “learned helplessness” was rampant in our classes, which is a symptom of a fixed mindset. “I know that I’m wrong, and there’s nothing that I can do about it. Collaboration just puts my errors on display for everyone,” was the kind of response we’ve got. (See here for one of our papers on this work.)
I believe that all the things Kevin sees going wrong in his classes really are happening. I believe he’s not seeing transfer that he might reasonably expect to see. I believe that he doesn’t see students trying to abstract across lower-level skills. But I suspect that the problem is the lack of a growth mindset. In our work, we saw Engineering students simply give up. They felt like they couldn’t learn, they couldn’t keep up, so they just memorized. I don’t know that that’s the cause of the problems that Kevin is seeing. In my work, I’ve often found that motivation and incentive are key to engagement and learning.