Posts tagged ‘educational psychology’
Cognitive Load as a Significant Problem in Learning Programming: Briana Morrison’s Dissertation Proposal
Briana Morrison is defending her proposal today. One chapter of her work is based on her ICER 2015 paper that won the Chairs Award for best paper (see post here). Good luck, Briana!
Title: Replicating Experiments from Educational Psychology to Develop Insights into Computing Education: Cognitive Load as a Significant Problem in Learning Programming
Human Centered Computing
College of Computing
Georgia Institute of Technology
Date: Wednesday, November 11, 2015
Time: 2 PM to 4 PM EDT
Location: TSRB 223
Dr. Mark Guzdial, School of Interactive Computing (advisor)
Dr. Betsy DiSalvo, School of Interactive Computing
Dr. Wendy Newstetter, School of Interactive Computing
Dr. Richard Catrambone, School of Psychology
Dr. Beth Simon, Jacobs School of Engineering at University of California San Diego and Principal Teaching and Learning Specialist, Coursera
Students often find learning to program difficult. This may be because the concepts are inherently difficult due to the fact that the elements of learning to program are highly interconnected. Instructors may be able to lower the complexity of learning to program by designing instructional materials that use educational psychology principles.
The overarching goal of this research is to gain more understanding and insight into the optimal conditions under which learning programming can be successful which is defined as students being able to apply their acquired knowledge and skills in new or familiar problem-solving situations. Cognitive load theory (CLT), and its associated effects, describe the role of the learner’s memory during the learning process. By minimizing undesirable loads within the instructional materials the learner’s memory can hold more relevant information, thereby improving the effectiveness of the learning process.
This proposal uses cognitive load theory to improve learning in programming. First an instrument for measuring cognitive load components within introductory programming was developed and initially validated. We have explored reducing the cognitive load by changing the modality in which students receive the learning material. This had no effect on novices’ retention of knowledge or their ability to transfer knowledge. We then attempted to reduce the cognitive load by adding subgoal labels to the instructional material. This had some effect on the learning gains under some conditions. Students who learned using subgoal labels demonstrated higher learning gains than the other conditions on the programming assessment task. We also explored using a low cognitive load assessment task, a Parsons problem, to measure learning gains. This low cognitive load assessment task proved more sensitive than the open ended programming assessment tasks in capturing student learning. Students who were given subgoal labels regardless of context transfer condition out performed those in the other conditions.
In my final, proposed study I change how we teach a programming construct through its format and content in order to reduce cognitive load. The changed construct is presumed to be a more natural cognitive fit for students based on previous research.
Human students need active learning and Econs learn from lecture: NYTimes Op-Ed in defense of lecture
I’m sympathetic to the author’s argument (linked below), that being able to understand an argument delivered as a lecture is difficult and worthwhile. Her characterization of active learning is wrong — it’s not “student-led discussion.” Actually, what she describes as good lecture is close to good active learning. Having students answering questions in discussion is good — but some students might disengage and not answer questions. Small group activities, peer led team learning, or peer instruction would be better to make sure that all students engage. But that’s not the critical flaw in her argument.
Being able to listen to a complicated lecture is an important skill — but students (at least in STEM, at least in the US) don’t have that skill. We can complain about that. We can reform primary and secondary schooling so that students develop that skill. But if we want these students to learn, the ones who are in our classes today, we should use active learning strategies.
Richard Thaler introduced the term “Econs” to describe the rational beings that inhabit traditional economic theory. (See a review of his book Misbehaving for more discussion on Econs.) Econs are completely rational. They develop the skills to learn from lecture because it is the most efficient way to learn. Unfortunately, we are not econs, and our classes are filled with humans. Humans are predictably irrational, as Daniel Ariely puts it. And there’s not much we can do about it. In his book Thinking, Fast and Slow, Daniel Kahneman complains that he knows how he is influenced by biases and too much System 1 thinking — and yet, he still makes the same mistakes. The evidence is clear that the students in our undergraduate classes today need help to engage with and learn STEM skills and concepts.
The empirical evidence for the value of active learning over lecture is strong (see previous post). It works for humans. Lecture probably works for Econs. If we could find enough of them, we could run an experiment.
In many quarters, the active learning craze is only the latest development in a long tradition of complaining about boring professors, flavored with a dash of that other great American pastime, populist resentment of experts. But there is an ominous note in the most recent chorus of calls to replace the “sage on the stage” with student-led discussion. These criticisms intersect with a broader crisis of confidence in the humanities. They are an attempt to further assimilate history, philosophy, literature and their sister disciplines to the goals and methods of the hard sciences — fields whose stars are rising in the eyes of administrators, politicians and higher-education entrepreneurs.
A similar argument to mine is below. This author doesn’t use the Humans/Econs distinction that I’m using. Instead, the author points out that lecturers too often teach only to younger versions of themselves.
I will grant that nothing about the lecture format as Worthen describes it is inherently bad. But Worthen’s elegy to a format that bores so many students reminds me of a bad habit that too many professors have: building their teaching philosophies around younger versions of themselves, who were often more conscientious, more interested in learning, and more patient than the student staring at his phone in the back of their classrooms.
Lecia Barker had a terrific paper in SIGCSE 2015 that I just recently had the chance to dig into. (See paper in ACM DL here.) Here’s the abstract:
Despite widespread development, research, and dissemination of teaching and curricular practices that improve student retention and learning, faculty often do not adopt them. This paper describes the first findings of a two-part study to improve understanding of adoption of teaching practices and curriculum by computer science faculty. The paper closes with recommendations for designers and developers of teaching innovations hoping to increase their chance of adoption.
I’ve published in this area before. Davide Fossati and I wrote a paper about the practices of CS teachers (based on interviews with about a dozen CS university teachers): how they made change, what convinced them to change, and how they decided if the change worked. (See blog post about this here.) The general theme was that these decisions rarely had an empirical basis.
Lecia and her co-authors went far beyond our study. She interviewed and observed 66 CS faculty from 36 institutions, explicitly chosen to represent a diverse set of schools. The result is the best picture I’ve yet seen of how CS faculty make decisions.
Lecia found more evidence of teachers using empirical evidence than we did, which was great to see. But whether students “liked” it or not was still the most critical variable:
On the other hand, if students don’t “like it,” faculty are unlikely to continue using a new practice. At a public research university, a professor said, “You can do something that you think, ‘Wow! If the learning experience was way better this term, the experiment really worked.’ And then you read your teaching reviews, and it’s like the students are pissed off because you did not do what they expected.”
Lecia discovered a reason not to adopt that I’d not heard before. She found that CS teachers filter out innovations that didn’t come from a context like their own. Those of us at research universities are filtered out by some teachers at teaching-oriented institutions:
Faculty trust colleagues who have similar teaching and research contexts, share attitudes toward students and teaching, or teach similar subjects. In describing what conference speakers he finds credible at SIGCSE, a professor at a private liberal arts university acknowledged, “I do have the anti- ‘Research One’ bias. Like if the speaker is somebody who teaches at <prestigious public research university>, the mental clout that I give them as a teacher—unless they’re a lecturer—I drop them a notch. When someone stands up to speak and they’re from a really successful teaching college <names several> or universities that have a real reputation of being great undergraduate teaching institutions, I give them a lot of merit.”
The part that I found most depressing (even if not surprising) is that research evidence did not matter at all in adopting new ways to teach:
Despite being researchers themselves, the CS faculty we spoke to for the most part did not believe that results from educational studies were credible reasons to try out teaching practices.
Lecia’s study is well done, and the paper is fascinating, but the overall picture is rather dismal. She points out many other issues that I’m not going into here, like the trade-off between cost and benefit of adopting a new practice, and about the need for specialized equipment in classrooms for some new practices. Overall, she finds that it’s really hard to get higher education CS faculty to adopt better practices. We reported on that in “Georgia Computes!” (see post here) but it’s even more disappointing when you see it in a large, broad study like this.
The New York Times weighs in on the argument about active learning versus passive lecture. The article linked below supports the proposition that college lectures unfairly advantage those students who are already privileged. (See the post about Miranda Parker’s work for a definition of what is meant by privilege.)
The argument that we should promote active learning over passive lecture has been a regular theme for me for a few weeks now:
- I argued in Blog@CACM that hiring ads and RPT requirements should be changed explicitly to say that teaching statements that emphasize active learning would be more heavily weighted (see post here).
- The pushback against this idea was much greater than I anticipated. I asked on Facebook if we could do this at Georgia Tech. The Dean of the College of Engineering was supportive. Other colleagues were strongly against it. I wrote a blog post about that pushback here.
- I wrote a Blog@CACM post over the summer about the top ten myths of computing education, which was the top-visited page at CACM during the month of July (see post here). I wrote that post in response to a long email thread on a College of Computing faculty mailing list, where I experienced that authority was able to sway CS faculty more than research results (blog post about that story here).
The NYTimes piece pushes on the point that this is not just an argument about quality of education. The argument is about what is ethical and just. If we value broadening participation in computing, we should use active learning methods and avoid lecture. If we lecture, we bias the class in favor of those who have already had significant advantages.
Thanks to both Jeff Gray and Briana Morrison who brought this article to my attention.
Yet a growing body of evidence suggests that the lecture is not generic or neutral, but a specific cultural form that favors some people while discriminating against others, including women, minorities and low-income and first-generation college students. This is not a matter of instructor bias; it is the lecture format itself — when used on its own without other instructional supports — that offers unfair advantages to an already privileged population.
The partiality of the lecture format has been made visible by studies that compare it with a different style of instruction, called active learning. This approach provides increased structure, feedback and interaction, prompting students to become participants in constructing their own knowledge rather than passive recipients.
Research comparing the two methods has consistently found that students over all perform better in active-learning courses than in traditional lecture courses. However, women, minorities, and low-income and first-generation students benefit more, on average, than white males from more affluent, educated families.
My Blog@CACM post this month makes a concrete proposal (quoted and linked below). We (all academic computing programs) should incentivize faculty to use active learning methods by evaluating teaching statements for hiring, tenure, and promotion more highly that reference active learning and avoid lecture.
On my Facebook page, I linked to the article and tagged our Dean of Engineering, the Vice-Provost for Undergraduate Education, and the RPT Chair for our College, and asked, “Can we do this at Georgia Tech?” The pushback on my Facebook page was the longest thread I’ve ever been part of on Facebook.
The issues raised were interesting and worth discussing:
- Would implementing this put at a disadvantage new PhD’s who have no teaching experience and don’t learn about active teaching? Yes, but that incentivizes those PhD programs to change.
- My blog post title is “Be It Resolved: Teaching Statements must embrace Active Learning and eschew Lecture.” I chose the word “eschew” deliberately. It doesn’t mean “ban.” It means “deliberately avoid using” which is what I meant. Lecture has its place — I wrote a blog post defending lecture which still gets viewed pretty regularly. The empirical evidence suggests that we should use active learning more than lecture for undergraduate STEM education.
- Should such a requirement for teaching statements emerge from faculty talking about it, or should it be done by administrative fiat? I lean toward the latter. As I’ve pointed out, CS faculty tend to respond to authority more than evidence. The administration should do the right thing, and deal with educating teachers (e.g., what are active learning methods first? how do we use them? even in large classes?) later. Faculty will learn the active learning methods in order to create those teaching statements. The incentive comes first.
- Lots of respondents thought I was saying that we should require all teaching to be active learning. I wasn’t, and I don’t know how to enforce that anyway. By evaluating teaching statements more heavily that emphasize active learning, we create an incentive, not a requirement.
- Some faculty pushed back, “How about students that like lecture? Tough luck for them?” Since we know that active learning is better, even for students who like lecture — yes.
- Several respondents suggested that active learning is just too hard, that faculty are over-stressed as it is. Faculty are over-stressed, but active learning isn’t that hard. In fact, it’s hard for faculty because they have to be quiet and listen in class more. It is hard to make change, but that’s the point of incentives. We start somewhere.
- The biggest theme in the thread is that we should first aim to get faculty to care about teaching and to take active steps to improve their teaching. I don’t think that’s enough. Libertarian paternalism (see Wikipedia page) suggests that we set the incentive at the minimal acceptable level (use of active learning) then encourage choice above that (choosing among the wide variety of active learning methods). We don’t want people to choose options that won’t be in the best interests of the largest number of people.
The discussion went on for four days (and hasn’t quite petered out yet). I do wonder if active learning methods will be forced upon faculty if we don’t willingly pick them up. The research evidence is overwhelming, with articles in Nature and hundreds of studies reviewed in the Proceedings of the National Academy of Sciences. How long before we get sued for teaching but not using the best teaching methods? One of the quotes in the blog post says, “At this point it is unethical to teach any other way.” We should take concrete steps towards doing the right thing, because it’s the right thing to do.
Here is something concrete that we in academia can do. We can change the way we select teachers for computer science and how we reward faculty.
All teaching statements for faculty hiring, promotion, and tenure should include a description of how the candidate uses active learning methods and explicitly reduces lecture.
We create the incentive to teach better. We might simply add a phrase to our job ads and promotion and tenure policies like, “Teaching statements will be more valued that describe how the candidate uses active learning methods and seeks to reduce lecture.”
NYTimes recently had a series of op-ed articles about the role of technology in our world, specifically, “Is Silicon Valley Saving the World, or Just Making Money?” The piece by Melinda Gates (quoted below) caught my attention because she’s invoking the desire to meet students’ “different learning styles” (see blog post on this theme, and why it leads to worse learning).
There’s an important issue here (beyond me critiquing Melinda Gates, who does important work that I admire). It’s not all technology. We need other disciplines as well. Educational psychologists should be informing these developers at Facebook to tell them, “Stop. That’s a bad idea.”
I was at a workshop last year at Stanford about how to grow more CS Education Research in the United States. Andrew Ng spoke to us about the research going on at Coursera. He was clearly not previously informed about the focus of the workshop. When asked, “Would you want to hire more PhD’s in CS Education?” he answered (my paraphrase), “Sure, but we just hire CS PhD’s, and they’re smart enough to pick up anything on-the-fly.” No, that’s wrong. CS is not a superset of all other disciplines. That belief is exactly the problem I see in the below quoted piece. Scholars in other areas do know things that CS PhD’s don’t, and they bring something unique to the table. Believing that it’s all technology is exactly why Silicon Valley gets accused of being more interested in money than having actual positive impact.
One of the biggest problems in American education is that teachers have to teach 30 students with different learning styles at the same time. Developers at Facebook, however, have built an online system that gives teachers the information and tools they need to design individualized lessons. The result is that teachers can spend their time doing what they’re best at: inspiring kids.
Briana Morrison is presenting the next stage of our work on subgoal labeled worked examples, with Lauren Margulieux. Their paper is “Subgoals, Context, and Worked Examples in Learning Computing Problem Solving.” As you may recall, Lauren did a terrific set of studies (presented at ICER 2012) showing how adding subgoal labels to videos of App Inventor worked examples had a huge effect on learning, retention, and transfer (see my blog post on this work here).
Briana and Lauren are now teaming up to explore new directions in educational psychology space and new directions in computing education research.
- In the educational psychology space, they’re asking, “What if you make the students generate the subgoal labels?” Past research has found that generating the subgoal labels, rather than just having them given to the students, is harder on the students but leads to more learning.
- They’re also exploring what if the example and the practice come from the same or different contexts (where the “context” here is the cover story or word problem story). For example, we might show people how to average test grades, but then ask them to average golf scores — that’s a shift in context.
- In the computing education research space, Briana created subgoal labeled examples for a C-like pseudocode.
One of the important findings is that they replicated the earlier study, but now in a text-based language rather than a blocks-based language. On average, subgoal labels on worked examples improve performance over getting the same worked examples without subgoal labels. That’s the easy message.
The rest of the results are much more puzzling. Being in the same context (e.g., seeing averaging test scores in the worked examples, then being asked to average test scores in the practice) did statiscally worse than having to shift contexts (e.g., from test scores to golf scores). Why might that be?
Generating labels did seem to help performance. The Generate group had the highest attrition. That make sense, because increased complexity and cognitive load would predict that more participants would give up. But that drop-our rate makes it hard make strong claims. Now we’re comparing everyone in the other groups to only “those who gut it out” in the Generate group. The results are more suspect.
There is more nuance and deeper explanations in Briana’s paper than I’m providing here. I find this paper exciting. We have an example here of well-established educational psychology principles not quite working as you might expect in computer science. I don’t think it puts the principles in question. It suggests to me that there may be some unique learning challenges in computer science, e.g., if the complexity of computer science is greater than in other studies, then it’s easier for us to reach cognitive overload. Briana’s line of research may help us to understand how learning computing is different from learning statistics or physics.