Posts tagged ‘cognitive science’
I drew on Cognitive Apprenticeship a lot in my dissertation — so much so that Carl Berger asked me at my proposal, “Are you testing Cognitive Apprenticeship as a model?” I had no idea how to respond, and 25 years later, I still don’t. How do you test a conceptual framework?
Cognitive apprenticeship, like situated learning, starts from the assumption that apprenticeship is a particularly effective form of education. Then it asks, “How do you offer an apprenticeship around invisible tasks?”
What I like about the essay linked below is that it places cognitive apprenticeship in a broader context. Apprenticeship isn’t always the best option (as discussed in the post about the Herb Simon paper).
Active listeners or readers, who test their understanding and pursue the issues that are raised in their minds, learn things that apprenticeship can never teach. To the degree that readers or listeners are passive, however, they will not learn as much as they would by apprenticeship, because apprenticeship forces them to use their knowledge. Moreover, few people learn to be active readers and listeners on their own, and that is where cognitive apprenticeship is critical–observing the processes by which an expert listener or reader thinks and practicing these skills under the guidance of the expert can teach students to learn on their own more skillfully.
Balancing cognition and motivation in computing education: Herbert Simon and evidence-based education
Education is a balancing act between optimally efficient instruction and motivating students. It’s not the same thing to meet the needs of the head and of the heart.
Shuchi Grover tweeted this interesting piece (quoted below) that reviews an article by Herb Simon (and John Anderson and Lynne Reder) which I hadn’t previously heard of. The reviewer sees Herb Simon as taking a stand against discovery-based, situated, and constructivist learning, and in favor of direct instruction. When I read the article, I saw a more subtle message. I do recommend reading the review piece linked below.
He [Herbert Simon] rejects discovery learning, and praises teacher instruction
When, for whatever reason, students cannot construct the knowledge for themselves, they need some instruction. The argument that knowledge must be constructed is very similar to the earlier arguments that discovery learning is superior to direct instruction. In point of fact, there is very little positive evidence for discovery learning and it is often inferior (e.g., Charney, Reder & Kusbit, 1990). Discovery learning, even when successful in acquiring the desired construct, may take a great deal of valuable time that could have been spent practicing this construct if it had been instructed. Because most of the learning in discovery learning only takes place after the construct has been found, when the search is lengthy or unsuccessful, motivation commonly flags.
Some cognitive scientists have been railing against the constructivist and situated approaches to learning for years. Probably the most important paper representing the cognitivist perspective is the Kirschner, Sweller, and Clark paper, “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching.” I talked about the Kirschner, Sweller, and Clark paper in this blog post with its implication for how we teach computer science.
The conclusion is pretty straightforward: Direct instruction is far more efficient than making the students work it out for themselves. Students struggling to figure something out for themselves does not lead to deeper learning or more transfer than simply telling students what they ought to do. Drill and practice is important. Learning in authentic, complex situations is unnecessary and often undesirable because failure increases with complexity.
The Anderson, Reder, and Simon article does something important that the famous Kirschner, Sweller, and Clark paper doesn’t — it talks about motivation. The words “motivation” and “interests” don’t appear anywhere in the Kirschner, Sweller, and Clark paper. Important attitudes about learning (like Carol Dweck’s fixed and growth mindsets, or Angela Duckworth’s grit) are not even considered.
In contrast, Anderson, Reder, and Simon understand that motivation is a critical part of learning.
Motivational questions lie outside our present discussion, but are at least as complex as the cognitive issues. In particular, there is no simple relation between level of motivation, on the one hand, and the complexity or realism of the context in which the learning takes place, on the other. To cite a simple example, learning by doing in the real-life domain of application is sometimes claimed to be the optimum procedure. Certainly, this is not true, when the tasks are life-threatening for novices (e.g., firefighting), when relevant learning opportunities are infrequent and unpredictable (e.g., learning to fly a plane in bad weather), or when the novice suffers social embarrassment from using inadequate skills in a real-life context (e.g., using a foreign language at a low level of skill). The interaction of motivation with cognition has been described in information-processing terms by Simon (1967, 1994). But an adequate discussion of these issues would call for a separate paper as long as this one.
There are, of course, reasons sometimes to practice skills in their complex setting. Some of the reasons are motivational and some reflect the special skills that are unique to the complex situation. The student who wishes to play violin in an orchestra would have a hard time making progress if all practice were attempted in the orchestra context. On the other hand, if the student never practiced as a member of an orchestra, critical skills unique to the orchestra would not be acquired. The same arguments can be made in the sports context, and motivational arguments can also be made for complex practice in both contexts. A child may not see the point of isolated exercises, but will when they are embedded in the real-world task. Children are motivated to practice sports skills because of the prospect of playing in full-scale games. However, they often spend much more time practicing component skills than full-scale games. It seems important both to motivation and to learning to practice one’s skills from time to time in full context, but this is not a reason to make this the principal mechanism of learning.
As a constructionist-oriented learning scientist, I’d go further with the benefits of a motivating context (which is a subset of what they’re calling a “complex setting”). When you “figure it out for yourself,” you have a different relationship to the domain. You learn about process, as well as content, as in learning what it means to be a scientist or how a programmer thinks. When you are engaged in the context, practice is no longer onerous but an important part of developing expertise — still arduous, but with meaning. Yasmin Kafai and Quinn Burke talk about changing students’ relationship with technology. Computer science shouldn’t just be about learning knowledge, but developing a new sense of empowerment with technology.
I’ve been wondering about what (I think) is an open research question about cognitivist vs. situationist approaches on lifelong learning. I bet you’re more likely to continue learning in a domain when you are a motivated and engaged learner. An efficiently taught but unmotivated learner is less likely to continue learning in the discipline, I conjecture.
While they underestimate the motivational aspect of learning, Anderson, Reder, and Simon are right about the weaknesses of an authentic context. We can’t just throw students into complex situations. Many students will fail, and those that succeed won’t be learning any better. They will learn slower.
Anderson, Reder, and Simon spend much of their paper critiquing Lave & Wenger’s Situated Learning. I draw on situated learning in my work (e.g., see post here) and reference it frequently in my book on Learner-Centered Computing Education, but I agree with their critique. Lave & Wenger are insightful about the motivation part, but miss on the cognitive part. Situated learning, in particular, provides insight into how learning is a process of developing identity. Lave & Wenger value apprenticeship as an educational method too highly. Apprenticeship has lots of weaknesses: inefficient, inequitable, and difficulty to scale.
The motivational component of learning is particularly critical in computing education. Most of our hot issues are issues of motivation:
- Female and under-represented minority students don’t learn differently than the white and Asian males who dominate computing. But they’re much less interested in learning computer science. “Computer science isn’t that difficult but wanting to learn it is.”
- Many students (especially female students) enter computer science class with low self-efficacy (see ICER 2016 award-winning paper). If they don’t believe they can do it, it’s not surprising that they don’t.
- We have too few computer science teachers because it is hard to motivate teachers to teach computer science.
- Students want authentic, complex learning situations, which is why we tend to value CS1 programming languages that are popular in industry. They don’t want practice and abstraction. Anderson, Reder, and Simon point out that more complex learning situations lead to more failure and lagging motivation.
The challenge to being an effective computing educator is to be authentic and complex enough to maintain motivation, and to use scaffolding to support student success and make learning more efficient. That’s the point of Phyllis Blumenfeld et al.’s “Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning.” (I’m in the “et al,” and it’s the most cited paper I’ve ever been part of.) Project-based learning is complex and authentic, but has the weaknesses that the cognitivists describe. Blumenfeld et al. suggest using technology to help students sustain their motivation and support their learning.
Good teaching is not just a matter of choosing the most efficient forms of learning. It’s also about motivating students to persevere, to tell them the benefits that make the efforts worthwhile. It’s about feeding the heart in order to feed the head.
I am posting this on the day that I am honored to “hood” Dr. Briana Morrison. “Hooding” is where doctoral candidates are given their academic regalia indicating their doctorate degree. It’s one of those ancient parts of academia that I find really cool. I like the way that the Wikiversity describes it: “The Hooding Ceremony is symbolic of passing the guard from one generation of doctors to the next generation of doctors.”
I’ve written about Briana’s work a lot over the years here:
- Her proposal is described here, “Cognitive Load as a significant problem in Learning Programming.”
- Her first major dissertation accomplishment was developing (with Dr. Brian Dorn) a measurement instrument for cognitive load.
- One of her bigger wins for her dissertation was showing that subgoal labels work for text languages too (ICER 2015).
- Another really significant result was showing that Parson’s Problems were a more sensitive measure of learning than asking students to write code in an assessment, and that subgoal labels make Parson’s Problems better, too.
- She worked a lot with Lauren Margulieux, so many of the links I listed when Dr. Margulieux defended are also relevant for Dr. Morrison.
- At ICER 2016, she presented a replication study of her first given vs. generated subgoals study.
But what I find most interesting about Briana’s dissertation work were the things that didn’t work:
- She tried to show a difference in getting program instruction via audio or text. She didn’t find one. The research on modality effects suggested that she would.
- She tried to show a difference between loop-and-a-half and exit-in-the-middle WHILE loops. Previous studies had found one. She did not.
These kinds of results are so cool to me, because they point out what we don’t know about computing education yet. The prior results and theory were really clear. The study was well-designed and vetted by her committee. The results were contrary to what we expected. WHAT HAPPENED?!? It’s for the next group of researchers to try to figure out.
The most interesting result of that kind in Briana’s dissertation is one that I’ve written about before, but I’d like to pull it all together here because I think that there are some interesting implications of it. To me, this is a Rainfall Problem kind of question.
Here’s the experimental set-up. We’ve got six groups.
- All groups are learning with pairs of a worked example (a completely worked out piece of code) and then a practice problem (maybe a Parson’s Problem, maybe writing some code). We’ll call these WE-P pairs (Worked Example-Practice). Now, some WE-P pairs have the same context (think of it as the story of a story problem), and some have different contexts. Maybe in the same context, you’re asked to compute the average tips for several days of tips as a barista. Maybe in a different context, you compute tips in the worked example, but you compute the average test score in the practice. In general, we predict that different contexts will be harder for the student than having everything the same.
- So we’ve got same context vs different context as one variable we’re manipulating. The other variable is whether the participants get the worked example with NO subgoal labels, or GENERATED subgoal labels, or the participant has to GENERATE subgoal labels. Think of a subgoal label as a comment that explains some code, but it’s the same comment that will appear in several different programs. It’s meant to encourage the student to abstract the meaning of the code.
In the GENERATE condition, the participants get blanks, to encourage them to abstract for themselves. Typically, we’d expect (for research in other parts of STEM with subgoal labels) that GENERATE would lead to more learning than GIVEN labels, but it’s harder. We might get cognitive overload.
In general, GIVEN labels beats out no labels. No problem — that’s what we expect given all the past work on subgoal labels. But when we consider all six groups, we get this picture.
Why would having the same context do worse with GIVEN labels than no labels? Why would the same context do much better with GENERATE labels, but worse when it’s different contexts?
So, Briana, Lauren, and Adrienne Decker replicated the experiment with Adrienne’s students at RIT (ICER 2016). And they found:
The same strange “W” pattern, where we have this odd interaction between context and GIVEN vs. GENERATE that we just don’t have an explanation for.
But here’s the really intriguing part: they also did the experiment with second semester students at RIT. All the weird interactions disappeared! Same context beat different context. GIVEN labels beat GENERATE labels. No labels do the worst. When students get enough experience, they figure things out and behave like students in other parts of STEM.
The puzzle for the community is WHY. Briana has a hypothesis. Novice students don’t attend to the details that they need, unless you change the contexts. Without changing contexts, students even GIVEN labels don’t learn because they’re not paying enough attention. Changing contexts gets them to think, “What’s going on here?” GENERATE is just too hard for novices — the cognitive load of figuring out the code and generating labels is just overwhelming for students, so they do badly when we’d expect them to do better.
Here we have a theory-conflicting result, that has been replicated in two different populations. It’s like the Rainfall Problem. Nobody expected the Rainfall Problem to be hard, but it was. More and more people tried it with their students, and still, it was hard. It took Kathi Fisler to figure out how to teach CS so that most students could succeed at the Rainfall Problem. What could we teach novice CS students so that they avoid the “W” pattern? Is it just time? Will all second semester students avoid the “W”?
Dr. Morrison gave us a really interesting dissertation — some big wins, and some intriguing puzzles for the next researchers to wrestle with. Briana has now joined the computing education research group at U. Nebraska – Omaha, where I expect to see more great results.
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.
We’re years into the MOOC phenomenon, and I’d hoped that we’d get past MOOC hype. But we’re not. The article below shows the same misunderstandings of learning and teaching that we heard at the start — misunderstandings that even MOOC supporters (like here and here) have stopped espousing.
The value of being in the front row of a class is that you talk with the teacher. Getting physically closer to the lecturer doesn’t improve learning. Engagement improves learning. A MOOC puts everyone at the back of the class, listening only and doing the homework.
In many ways, we have a romanticized view of college. Popular portrayals of a typical classroom show a handful of engaged students sitting attentively around a small seminar table while their Harrison Ford-like professor shares their wisdom about the world. We all know the real classroom is very different. Especially in big introductory classes — American history, U.S. government, human psychology, etc. — hundreds of disinterested, and often distracted, students cram into large impersonal lecture halls, passively taking notes, occasionally glancing up at the clock waiting for the class to end. And it’s no more engaging for the professor. Usually we can’t tell whether students are taking notes or updating their Facebook page. For me, everything past the ninth row was distance learning. A good online platform puts every student in the front row.
Important paper at SIGCSE 2015: Transferring Skills at Solving Word Problems from Computing to Algebra Through Bootstrap
I was surprised that this paper didn’t get more attention at SIGCSE 2015. The Bootstrap folks are seeing evidence of transfer from the computing and programming activities into mathematics performance. There are caveats on the result, so these are only suggestive results at this time.
What I’d like to see in follow-up studies is more analysis of the students. The paper cited below describes the design of Bootstrap and why they predict impact on mathematics learning, and describes the pre-test/post-test evidence of impact on mathematics. When Sharon Carver showed impact of programming on problem-solving performance (mentioned here), she looked at what the students did — she showed that her predictions were met. Lauren Margulieux did think-aloud protocols to show that students were really saying subgoal labels to themselves when transferring knowledge (see subgoal labeling post). When Pea & Kurland looked for transfer, they found that students didn’t really learn CS well enough to expect anything to transfer — so we need to demonstrate that they learned the CS, too.
Most significant bit: Really cool that we have new work showing potential transfer from CS learning into other disciplines.
Many educators have tried to leverage computing or programming to help improve students’ achievement in mathematics. However, several hopes of performance gains—particularly in algebra—have come up short. In part, these efforts fail to align the computing and mathematical concepts at the level of detail typically required to achieve transfer of learning. This paper describes Bootstrap, an early-programming curriculum that is designed to teach key algebra topics as students build their own videogames. We discuss the curriculum, explain how it aligns with algebra, and present initial data showing student performance gains on standard algebra problems after completing Bootstrap.
In Josh Tenenberg’s lead article in the September 2014 ACM Transactions on Computing Education (linked below), he uses this blog, and in particular, this blog post on research questions, as a foil for exploring what questions we ask in computing education research. I was both delighted (“How wonderful! I have readers who are thinking about what I’m writing!”) and aghast (“But wait! It’s just a blog post! I didn’t carefully craft the language the way I might a serious paper!”) — but much more the former. Josh is kind in his consideration, and raises interesting issues about our perspectives in our research questions.
I disagree with one part of his analysis, though. He argues that my conception of computing education (“the study of how people come to understand computing”) is inherently cognitivist (centered in the brain, ignoring the social context) because of the word “understand.” Maybe. If understanding is centered in cognition, yes, I agree. If understanding is demonstrated through purposeful action in the world (i.e., you understand computing if you can do with computing what you want), then it’s a more situated definition. If understanding is a dialogue with others (i.e., you understand computing if you can communicate about computing with others), then it’s more of a sociocognitive definition.
The questions he calls out are clearly cognitivist. I’m guilty as charged — my first PhD advisor was a cognitive scientist, and I “grew up” as the learning science community was being born. That is my default position when it comes to thinking about learning. But I think that my definition of the field is more encompassing, and in my own work, I tend toward thinking more about motivation and about communities of practice.
Asking significant research questions is a crucial aspect of building a research foundation in computer science CS education. In this article, I argue that the questions that we ask are shaped by internalized theoretical presuppositions about how the social and behavioral worlds operate. And although such presuppositions are essential in making the world sensible, at the same time they preclude carrying out many research studies that may further our collective research enterprise. I build this argument by first considering a few proposed research questions typical of much of the existing research in CS education, making visible the cognitivist assumptions that these questions presuppose. I then provide a different set of assumptions based on sociocultural theories of cognition and enumerate some of the different research questions to which these presuppositions give rise. My point is not to debate the merits of the contrasting theories but to demonstrate how theories about how minds and sociality operate are imminent in the very questions that researchers ask. Finally, I argue that by appropriating existing theory from the social, behavioral, and learning sciences, and making such theories explicit in carrying out and reporting their research, CS education researchers will advance the field.