Posts tagged ‘learning sciences’
Learning Curves, Given vs Generated Subgoal Labels, Replicating a US study in India, and Frames vs Text: More ICER 2016 Trip Reports
My Blog@CACM post for this month is a trip report on ICER 2016. I recommend Andy Ko’s excellent ICER 2016 trip report for another take on the conference. You can also see the Twitter live feed with hashtag #ICER2016.
I write in the Blog@CACM post about three papers (and reference two others), but I could easily write reports on a dozen more. The findings were that interesting and that well done. I’m going to give four more mini-summaries here, where the results are more confusing or surprising than those I included in the CACM Blog post.
This year was the first time we had a neck-and-neck race for the attendee-selected award, the “John Henry” award. The runner-up was Learning Curve Analysis for Programming: Which Concepts do Students Struggle With? by Kelly Rivers, Erik Harpstead, and Ken Koedinger. Tutoring systems can be used to track errors on knowledge concepts over multiple practice problems. Tutoring systems developers can show these lovely decreasing error curves as students get more practice, which clearly demonstrate learning. Kelly wanted to see if she could do that with open editing of code, not in a tutoring system. She tried to use AST graphs as a sense of programming “concepts,” and measure errors in use of the various constructs. It didn’t work, as Kelly explains in her paper. It was a nice example of an interesting and promising idea that didn’t pan out, but with careful explanation for the next try.
I mentioned in this blog previously that Briana Morrison and Lauren Margulieux had a replication study (see paper here), written with Adrienne Decker using participants from Adrienne’s institution. I hadn’t read the paper when I wrote that first blog post, and I was amazed by their results. Recall that they had this unexpected result where changing contexts for subgoal labeling worked better (i.e., led to better performance) for students than keeping students in the same context. The weird contextual-transfer problems that they’d seen previously went away in the second (follow-on) CS class — see below snap from their slides. The weird result was replicated in the first class at this new institution, so we know it’s not just one strange student population, and now we know that it’s a novice problem. That’s fascinating, but still doesn’t really explain why. Even more interesting was that when the context transfer issues go away, students did better when they were given subgoal labels than when they generated them. That’s not what happens in other fields. Why is CS different? It’s such an interesting trail that they’re exploring!
Mike Hewner and Shitanshu Mishra replicated Mike’s dissertation study about how students choose CS as a major, but in Indian institutions rather than in US institutions: When Everyone Knows CS is the Best Major: Decisions about CS in an Indian context. The results that came out of the Grounded Theory analysis were quite different! Mike had found that US students use enjoyment as a proxy for ability — “If I like CS, I must be good at it, so I’ll major in that.” But Indian students already thought CS was the best major. The social pressures were completely different. So, Indian students chose CS — if they had no other plans. CS was the default behavior.
One of the more surprising results was from Thomas W. Price, Neil C.C. Brown, Dragan Lipovac, Tiffany Barnes, and Michael Kölling, Evaluation of a Frame-based Programming Editor. They asked a group of middle school students in a short laboratory study (not the most optimal choice, but an acceptable starting place) to program in Java or in Stride, the new frame-based language and editing environment from the BlueJ/Greenfoot team. They found no statistically significant differences between the two different languages, in terms of number of objectives completed, student frustration/satisfaction, or amount of time spent on the tasks. Yes, Java students got more syntax errors, but it didn’t seem to have a significant impact on performance or satisfaction. I found that totally unexpected. This is a result that cries out for more exploration and explanation.
There’s a lot more I could say, from Colleen Lewis’s terrific ideas to reduce the impact of CS stereotypes to a promising new method of expert heuristic evaluation of cognitive load. I recommend reviewing the papers while they’re still free to download.
Preview ICER 2016: Ebooks Design-Based Research and Replications in Assessment and Cognitive Load Studies
The International Computing Education Research (ICER) Conference 2016 is September 8-12 in Melbourne, Australia (see website here). There were 102 papers submitted, and 26 papers accepted for a 25% acceptance rate. Georgia Tech computing education researchers are justifiably proud — we submitted three papers to ICER 2016, and we had three acceptances. We’re over 10% of all papers at ICER 2016.
One of the papers extends the ebook work that I’ve reported on here (see here where we made them available and our paper on usability and usage from WiPSCE 2015). Identifying Design Principles for CS Teacher Ebooks through Design-Based Research (click on the title to get to the ACM DL page) by Barbara Ericson, Kantwon Rogers, Miranda Parker, Briana Morrison, and I use a Design-Based Research perspective on our ebooks work. We describe our theory for the ebooks, then describe the iterations of what we designed, what happened when we deployed (data-driven), and how we then re-designed.
Two of our papers are replication studies — so grateful to the ICER reviewers and communities for seeing the value of replication studies. The first is Replication, Validation, and Use of a Language Independent CS1 Knowledge Assessment by Miranda Parker, me, and Shelly Engleman. This is Miranda’s paper expanding on her SIGCSE 2016 poster introducing the SCS1 validated and language-independent measure of CS1 knowledge. The paper does a great survey of validated measures of learning, explains her process, and then presents what one can and can’t claim with a validated instrument.
The second is Learning Loops: A Replication Study Illuminates Impact of HS Courses by Briana Morrison, Adrienne Decker, and Lauren Margulieux. Briana and Lauren have both now left Georgia Tech, but they were still here when they did this paper, so we’re claiming them. Readers of this blog may recall Briana and Lauren’s confusing results from SIGCSE 2016 result that suggest that cognitive load in CS textual programming is so high that it blows away our experimental instructional treatments. Was that an aberration? With Adrienne Decker’s help (and student participants), they replicated the study. I’ll give away the bottom line: It wasn’t an aberration. One new finding is that students who did not have high school CS classes caught up with those who did in the experiment, with respect to understanding loops
We’re sending three of our Human-Centered Computing PhD students to the ICER 2016 Doctoral Consortium. These folks will be in the DC on Sept 8, and will present posters to the conference on Sept 9 afternoon.
- Barbara Ericson will be presenting her results with Dynamically Adaptive Parsons Problems. I’ve seen some of the pilot study results from this summer, and they’re fascinating.
- Amber Solomon is just starting her second year working with me. She did the evaluation on the AR Design Studio classroom. She (and I) is fascinated by Steve Cooper’s results from ICER 2015 where spatial reasoning training influenced CS performance and reduced SES differences. She’s been doing a study on CS grades, SES, and spatial reasoning in a non-majors class. She’ll be presenting on The Role of Spatial Reasoning in Learning Computer Science.
- Kayla DesPortes works with my colleague Betsy DiSalvo on the learning that happens in MakerSpaces. She’s designing new kinds of physical interfaces to reduce cognitive load and improve learning when working with electronics, which she’ll be talking about at her poster: Learning and Collaboration in Physical Computing.
Seeking Collaborators for a Study of Achievement Goal Theory in CS1: Guest blog post by Daniel Zingaro
I have talked about Dan’s work here before, such as his 2014 award-winning ICER paper and his Peer Instruction in CS website. I met with Dan at the last SIGCSE where he told me about the study that he and Leo Porter were planning. Their results are fascinating since they are counter to what Achievement Goal Theory predicts. I invited him to write a guest blog post to seek collaborators for his study, and am grateful that he sent me this.
Why might we apply educational theory to our study of novice programmers? One core reason lies in theory-building: if someone has developed a general learning theory, then we might do well to co-opt and extend it for the computing context. What we get for free is clear: a theoretical basis, perhaps with associated experimental procedures, scales, hypotheses, and predictions. Unfortunately, however, there is often a cost in appropriating this theory: it may not replicate for us in the expected ways.
Briana Morrison’s recent work nicely highlights this point. In two studies, Briana reports her efforts to replicate what is known about subgoals and worked examples. Briefly, a worked example is a sample problem whose step-by-step solution is given to students. And subgoals are used to break that solution into logical chunks to hopefully help students map out the ways that the steps fit together to solve the problem.
Do subgoals help? Well, it’s supposed to go like this, from the educational psychology literature: having students generate their own labeled goals is best, giving students the subgoal labels is worse, and not using subgoals at all is worse still. But that isn’t what Briana found. For example, Briana reports  that, on Parsons puzzles, students who are given subgoal labels do better than both those who generate their own subgoal labels and those not given subgoals at all. Why the differences? One possibility is that programming exerts considerable cognitive load on the learner, and that the additional load incurred by generating subgoal labels overloads the student and harms learning.
The point here is that taking seriously the idea of leveraging existing theory requires concomitant attention to how and why the theory may operate differently in computing.
My particular interest here is in another theory from educational psychology: achievement goal theory (AGT). AGT studies the goals that students adopt in achievement situations, and the positive and negative consequences of those goals in terms of educationally-relevant outcomes. AGT zones in on two main goal types: mastery goals (where performance is defined intrapersonally) and performance goals (where performance is defined normatively in comparison to others).
Do these goals matter? Well, it’s supposed to go roughly like this: mastery goals are positively associated with many outcomes of value, such as interest, enjoyment, self-efficacy, and deep study strategies (but not academic performance); performance goals, surprisingly and confusingly, are positively associated with academic performance. But, paralleling the Briana studies from above, this isn’t what we’ve found in CS. With Leo Porter and my students, we’ve been studying goal-outcome links in novice CS students. We’ve found, contrary to theoretical expectations, that performance goals appear to be null or negative predictors of performance, and that mastery goals appear to be positive predictors of performance [2,3].
We are now conducting a larger study of achievement goals and outcomes of CS1 students — larger than that achievable with the couple of institutions to which we have access on our own. We are asking for your help.
The study involves administering two surveys to students in a CS1 course. The first survey, at the beginning of the semester, measures student achievement goals. The second survey, close to the end of the semester, measures potential mediating variables. We plan to collect exam grade, interest in CS, and other outcome variables.
The hope is that we can conduct a multi-institutional study of a variety of CS1 courses to strengthen what we know about achievement goals in CS.
Please contact me at daniel dot zingaro at utoronto dot ca if you are interested in participating in this work. Thanks!
 Briana Morrison. Subgoals Help Students Solve Parsons Problems. SIGCSE, 2016. ACM DL link.
 Daniel Zingaro. Examining Interest and Performance in Computer Science 1: A Study of Pedagogy and Achievement Goals. TOCE, 2015. ACM DL link.
 Daniel Zingaro and Leo Porter. Impact of Student Achievement Goals on CS1 Outcomes. SIGCSE, 2016. ACM DL link.
I don’t often link to Quora, but when it’s Steven Pinker pointing out the relationship between our human nature to educational goals, it’s worth it.
One potential insight is that educators begin not with blank slates but with minds that are adapted to think and reason in ways that may be at cross-purposes with the goals of education in a modern society. The conscious portion of language consists of words and meanings, but the portion that connects most directly to print consists of phonemes, which ordinarily are below the level of consciousness. We intuitively understand living species as having essences, but the theory of evolution requires us to rethink them as populations of variable individuals. We naturally assess probability by dredging up examples from memory, whereas real probability takes into account the number of occurrences and the number of opportunities. We are apt to think that people who disagree with us are stupid and stubborn, while we are overconfident and self-deluded about our own competence and honesty.
I’ve known Dan Hickey for many years, and got to spend some time with him at Indiana when I visited there a couple years ago. He’s dealing with an issue in this blog post that is critical to CS Education. If we want students to value computing, it has to be valued and promoted in their families and communities. How do we get engagement at a beyond-school level in computing education?
These issues of trajectories and non-participation in STEM learning have personal relevance for me and my own family. I was quite pleased a few years ago when my son Lucas enrolled in a computer programming class in high school. I never learned to program myself and these days it I find it quite a handicap. While I bought an Apple II+ computer in 1982 (!) and taught myself BASIC, an instructional technology professor discouraged me from delving too deeply into technology or programming (because “it changes too often”). While I still want to learn how to code, my non-participation in programming clearly helped define my trajectory towards a Ph.D in Psychology and satisfying career as a Learning Scientist.Unfortunately, the curriculum in my son’s programming class was like the typical secondary computer science instruction that Mark Guzdial chronicles in his Computing Education blog. The coding worksheets seemed to have been haphazardly created to match various videos located on the web. My son wanted to use the much more professional videos and exercises that we were able to access via my university’s account at Lynda.com, but his teacher insisted that my son complete the worksheets as well (so teacher could grade them).
My Blog@CACM post this month is on the AAAS symposium I attended on undergraduate STEM education (see post here). The symposium set up for me a contrast between computing education and other STEM education. In math and science education, faculty are more likely to get continuing professional development and to value education more than CS faculty.
Why is it different in CS? In the blog post, I suggest that part of the issue is maturation of the field. But I have another hypothesis — I suggest that most CS teachers, especially at the undergraduate level, don’t think of themselves as teachers.
In my book Learner-Centered Design of Computing Education, I use Lave & Wenger’s situated learning theory as a lens for understanding motivations to pursue computing education. Lave & Wenger say every learner aims to join a community of practice. Learners start out on the periphery of the community, and work their way towards the center, adopting the skills, values, and knowledge that those in the center hold. They might need to take classes because that’s what the community values, or maybe they do an apprenticeship. The community of practice provides the learner and the practitioners a sense of identity: “I belong with this group. I do this practice. This is who I am.”
Lijun Ni taught me the value of teacher identity. Someone who says “I’m a math teacher” (for example) will join math teacher organizations, will seek out professional development, and will more likely be retained longer as a teacher. That’s their identity.
I believe that many science and math teachers (even at the undergraduate level) feel a sense of identity as teachers. Even at research universities, those teaching the intro courses in mathematics and science are likely teachers-first. They know that they are mostly no preparing future mathematicians, biologists, chemists, and physicists. They are preparing students for their chosen professions, perhaps in engineering, medicine, or computer science. The math and science teachers belong to a community of practice of teachers, e.g., they have a goal to be like the best teachers in their profession. They have an identity as teachers, e.g., they strive to be better math and science teachers.
I suspect that CS teachers feel a sense of identity as software developers. They see themselves as programmers primarily. They see themselves as producing future programmers. They take pride in what they can do with code. They have a sense of guardianship — they want the best and brightest in their field.
There’s a difference between CS teachers as programmers vs CS teachers. Programmers train other programmers. They learn new programming languages, new techniques of programming, the latest tools. Teachers teach everyone, and they learn how to be better at teaching. We need CS teachers to be teachers. It’s less important that they know the latest industry gadgets. It’s more important that they learn how to teach “all” about CS, and how to teach that CS better.
When Grady Booch came to SIGCSE 2007, I was surprised at how excited everyone was — people still talk about that visit (e.g., see the explanation for the BJC approach to computing). I realized that, for most of the people in the room, Grady was a role model. He was at the center of community that they most cared about. Note that Grady is not a teacher. He’s an exceptional software engineer.
There are serious ramifications of a teacher with an identity as a software engineer. I had a discussion a few months ago with one of our instructors, who told me, “I just don’t get why women would even want to be in computer science. Working in a cubicle is not a great place for women to be! They should get a better job.” I was shocked. I didn’t tackle the gender issues first. I started out trying to convince him that computer science doesn’t just lead to a cubicle. You could study computer science to become something other than a software developer, to work somewhere other than a cubicle. He wasn’t buying my argument. I realized that those cubicle jobs are the ones he wants to prepare students for. That’s where he imagines the best programmers working. He doesn’t want to teach computer science for whatever the students need it for. He prepares future programmers. That’s how he defines his job — a master software engineer with apprentice software engineers.
I am calling out undergraduate CS teachers in this post, but I suspect that many high school CS teachers see themselves as software developers (or as trainers of software developers), more than as teachers of computer science. I hear about high school CS teachers who proudly post on the wall the t-shirts of the tech companies who employ their former students. That’s a software developer focus, an apprenticeship focus. That’s not about teaching CS for all.
What would it take to shift the community of practice of CS teachers to value teaching over software development? It’s an important change in perspective, especially if we care about CS for all. Not all of our students are aiming for jobs in software development.
How did other STEM disciplines do it? How did they develop a culture and community of practice around teaching?
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.