Subgoal labelling influences student success and retention in CS

I have written a lot about subgoal labeling in his blog. Probably the best way to find it all is to see the articles I wrote about Lauren’s graduation (link here) and Briana’s (link here). They have continued their terrific work, and have come out with their most impressive finding yet.

In work with Adrienne Decker, they have shown that subgoal labeling reduces the rate at which students fail or drop out of introductory computer science classes: “Reducing withdrawal and failure rates in introductory programming with subgoal labeled worked examples” (see link here). The abstract is below.

We now have evidence that subgoal labelling lead to better learning, better transfer, work in different languages, work in both text and block-based programming languages, and work in Parsons Problems. Now, we have evidence that their use leads to macro effects, like improved student success and retention. We also see a differential impact — the students most at risk of failing are the ones who gain the most.

This is a huge win.

Abstract

Background: Programming a computer is an increasingly valuable skill, but dropout and failure rates in introductory programming courses are regularly as high as 50%. Like many fields, programming requires students to learn complex problem-solving procedures from instructors who tend to have tacit knowledge about low-level procedures that they have automatized. The subgoal learning framework has been used in programming and other fields to breakdown procedural problem solving into smaller pieces that novices can grasp more easily, but it has only been used in short term interventions. In this study, the subgoal learning framework was implemented throughout a semester-long introductory programming course to explore its longitudinal effects. Of 265 students in multiple sections of the course, half received subgoal-oriented instruction while the other half received typical instruction.

Results: Learning subgoals consistently improved performance on quizzes, which were formative and given within a week of learning a new procedure, but not on exams, which were summative. While exam performance was not statistically better, the subgoal group had lower variance in exam scores and fewer students dropped or failed the course than in the control group. To better understand the learning process, we examined students’ responses to open-ended questions that asked them to explain the problem-solving process. Furthermore, we explored characteristics of learners to determine how subgoal learning affected students at risk of dropout or failure.

Conclusions: Students in an introductory programming course performed better on initial assessments when they received instructions that used our intervention, subgoal labels. Though the students did not perform better than the control group on exams on average, they were less likely to get failing grades or to drop the course. Overall, subgoal labels seemed especially effective for students who might otherwise struggle to pass or complete the course.

Keywords: Worked examples, Subgoal learning, Programming education, Failure rates

Managing CS major enrollment boom with a lottery: “A lottery, by definition, is fair.”

I am excited to see that the University of California, San Diego is now managing their over-enrollment in the computer science major with a lottery — see the article here.

Instead of enrolling students holistically or based on GPA, the department selects at random — assuming they exceed the 3.3 CSE GPA threshold. With the lottery system, all students are equally considered despite differences in their experience, drive, and ability.

When asked about the implications of the new system — and possible disadvantage to high-performing students — CSE Chair Dean Tullsen explained, “a lottery, by definition, is fair.”

“I think there’s this false assumption that the students who work harder are the ones who are getting the 4.0s, that hard work directly translates to a higher grade. [The lottery system will] admit a lot of hard-working students who weren’t getting in before,” CSE Vice-Chair for Undergraduate Education Christine Alvarado added.

This is a much more fair system than simply allowing in the top GPA students. It probably doesn’t make Tech companies happier, but it’s not clear that it makes them less happy. They will still get lots of potential employees who are above the bar. Those employees will likely be more diverse than the graduates being produced from CS programs today. The students getting the top grades in the early classes are typically those with more opportunity to learn CS, more wealth, and more privilege. A lottery says that anyone who is prepared for the courses can take them.

Why do students study computing, especially programming

Alan Kay asked me in a comment on my blog post from Monday:

You and your colleagues have probably done a survey over the years, but it would be useful to see one or two examples, and especially one from the present time of “why are you currently studying computing, especially programming?”

It would be illuminating — and very important — to see the reasons, and especially the percentage who say: “to learn and understand and do computing and programming”.

A half-dozen papers sprang to mind. Rather than type them into a teeny tiny response box, I’m going to put them here. This is not a comprehensive survey. It’s the papers that occurred to me at 8:30 am EDT in response to Alan’s query.

The biggest recent study of this question is the Santo, Vogel, and Ching white paper CS for What? Diverse Visions of Computer Science Education in Practice (find it here). This paper is particularly valuable because it starts K-12 — what are the reasons for studying CS in school?

The most recent paper I know on this topic (and there were probably new ones at RESPECT and SIGCSE 2020 that I haven’t found yet) is this one from Koli, It’s like computers speak a different language: Beginning Students’ Conceptions of Computer Science (find it here). I liked this one because I most resonated with the “Creator” perspective, but I design today for those with the “Interpreter” perspective.

Alan particularly asked what we had done in our group. We started asking these questions when we were doing Media Computation (here’s a 2005 paper where we got those answers from Georgia Tech and Gainesville College students — GT students mostly wanted to know how to use a computer better and then get a good grade, while Gainesville students wanted to know what programming was). We got different answers from the follow-on course MediaComp Data Structures course where we started seeing a real split in views (see Lana Yarosh’s paper here). When we were doing “Georgia Computes!”, we did a statewide survey in 2010 to understand influences on students’ persistence in CS (see paper here). This is important to read, to realize that it’s not just about ability and desire. Women and BIPOC students are told that they don’t belong, and they need particular attention and encouragement to get them to go on, even if they believe they could and should. Probably the study from my group most explicitly on this question is Mike Hewner’s Undergraduate conceptions of the field of computer science (see paper here).

Two of my former students, but not with me, developed a validated computing attitudes survey (Tew, Dorn, and Schneider, paper here). Here, they ask experts what CS is, then use the same instrument to ask students what CS is, so that they can compare how “expert-like” the students answers are.

Not from my research group, but a really important paper in this space is Maureen Biggers et al’s Student perceptions of computer science: a retention study comparing graduating seniors with CS leavers (see link here). Most studies look at those who stay in CS. Maureen and her team also interviewed those who left, and how their perception of the field differed.

There are so many others, like the “Draw a Computer Scientist” task to elicit what the field is about (see example paper here and here). I particularly like Philip Guo’s groups paper on “conversational programmers” — people who study programming so that they can talk to programmers, not to ever program or to understand programming (see CHI’16 paper here).

Here’s what I think is the main answer to Alan’s question: Yes, it’s changed. But there are some specific answers that are consistent. Yes, some want to learn programming. Others want to learn programming as part of general computer skills.

I’m going to stop here and push this out before my meetings start for the day. I invite readers to add their favorite papers answering this question below.

How do teachers teach recursion with embodiment, and why won’t students trace their programs: ICLS 2020 Preview

This coming week was supposed to be the International Conference of the Learning Sciences (ICLS) 2020 in Nashville (see conference website here). But like most conferences during the pandemic, the face-to-face meeting was cancelled (see announcement here). The on-line sessions are being announced on the ICLS2020 Twitter feed here.

I’m excited that two of my students had papers accepted at ICLS 2020. I haven’t published at ICLS since 2010. It’s nice to get back involved in the learning sciences community. Here’s a preview of their papers.

How do teachers teach recursion with embodiment

I’ve written here about Amber Solomon’s work on studying the role of space and embodiment in CS learning. This is an interesting question. We live in a physical world and think in terms of physical things, and we have to use that to understand the virtual, mostly invisible, mostly non-embodied world of computing. At ICER 2018, she used a taxonomy of gestures used in science learning to analyze the gestures she saw in a high school computer science classroom (see link here). Last summer at ITiCSE, she published a paper on how making CS visible in the classroom (through gesture and augmented reality) may reduce defensive climate (see link here). In her dissertation, she’s studying how teachers teach recursion and how learners learn recursion, with a focus on spatial symbol systems.

Her paper at ICLS 2020 is the first of these studies: Embodied Representations in Computing Education: How Gesture,Embodied Language, and Tool Use Support Teaching Recursion. She watched hours of video of teachers teaching recursion, and did a deep dive on two of them.

I’m fascinated by Amber’s findings. Looking at what teachers say and gesture about recursion from the perspective of physical embodiment, I’m amazed that students ever learn computer science. There are so many metaphors and assumptions that we make. One of the teachers says, when explaining a recursive function:

“Then it says “… “now I have to call.”

Let’s think about this from the perspective of the physical world (which is where we all start when trying to understand computing):

• What does it mean for a function to “say” something?
• The function “says” things, but I “call”? Who is the agent in this explanation, the function or me? It’s really the computer with the agency, but that doesn’t get referenced at all.
• Recursion is typically explained as a function calling itself. We typically “call” something that is physically distant from us. If a function is re-invoking itself, why does it have to “call” as if at a distance?

For most computer scientists, this may seem like explaining that the sky is blue or that gravel exists. It’s obvious what all of this means, isn’t it? It is to us, but we had to learn it. Maybe not everyone does. Remember how very few students take or succeed at computer science (for example, see this blog post), and what enormously high failure and drop-out rates we have in CS. Maybe only the students who pick up on these metaphors are the ones succeeding?

Why won’t students trace their programs?

Katie Cunningham’s first publication as a PhD student was her replication and extension of the Leeds Working group study, showing that students who trace program code successfully line-by-line are able to answer more accurately questions about the code (see blog post here). But one of her surprising results was that students who start tracing and give up do worse on prediction questions than those students who never traced at all. In her ITICSE 2019 paper (see post here), she got the chance to ask those students who stopped tracing why they did. She was extending that with a think-aloud protocol, when something unusual happened. Two data science students, who were successful at programming, frankly refused to trace code.

Her paper “I’m not a computer”: How identity informs value and expectancy during a programming activity is an exploration of why students would flat out refuse to trace code — and yet successfully program. She uses Eccle’s Expectancy Value Theory (which comes up pretty often in our thinking, see this blog post) to describe why the cost of tracing outweighs the utility for these students, which is defined in terms of their sense of identity — what they see themselves doing in the future. Sure, there will be some programs that they won’t be able to debug or understand because they won’t trace line-by-line. But maybe they’ll never actually have to deal with code that complex. Is this so bad?

Katie’s live session is 2:00-2:40pm Eastern time on June 23. The video link will be available on the conference website to registered attendees. A pre-print version of her paper is available here.

Both of these papers give us new insight into the unexpected consequences of how we teach computing. We currently expect students to figure out how their teachers are relating physical space and computation, through metaphors that we don’t typically explain. We currently teach computing expecting students to be able to trace code line-by-line, though some students will not do it (and maybe don’t really need to). If we want to grow who can succeed at computing education, we need to think through who might be struggling with how we’re teaching now, and how we might do better.

Becoming anti-racist: Learning about race in CS Education

I don’t usually invite external review on my blog posts for CACM, but I did this month because it’s such an important topic and I know too little about it — “CS Teachers, It’s (Past) Time To Learn About Race” (see link here). Many thanks to Melissa Perez, Carl Haynes, Leigh Ann DeLyser, Betsy DiSalvo, Leo Porter, Chad Jenkins, Wes Weimer, Barbara Ericson, Matthew Guzdial, Katie Guzdial, and Manuel Perez Quinones.

We have to change CS Education. We do not talk enough about BIPOC (Black, Indigenous, and People of Color) students and faculty in CS education. We have to reflect that Black Lives Matter in our teaching practice. We have to become explicitly anti-racist (a term I just learned this last week, see the book link here) — actively seeking to address historic and systemic inequities (see piece at CNN too).

One of the reviewer’s comments was that, by offering some small suggestions (like changing how we grade), I might dissuade people from bigger changes. It’s a valid concern. I’m hoping that people will take me at my word: I’m just learning here, and I hope that you will educate me (and each other) by sharing other ideas and resources. Please do share more ideas in the comments to this post.

Here are a few more that have come my way since that post that I wanted to share:

• Ron Eglash has written up a terrific list of strategies for address issues of racism in technology — see link here.
• Melissa Perez, a PhD student working with Barb Ericson, pointed out that it’s not enough to bring more people into CS education if we don’t change what we’re doing in CS. For example, we have to consider the problem of using biased training data for machine learning training. She recommends this article for considering the ethics of what we do in CS, besides how we teach CS. We need to integrate ethics across CS education.
• Carl Haynes, also a PhD student working with Barb, recommends this book on intersectionality (see link here).
• Manuel Perez Quinones recommends this Best Paper awardee from CHI 2020 on “Critical Race Theory for HCI” (see link here).
• Kamau Bobb gave a talk at CornellTech in January “Unpacking Equity: To Code + Beyond” which is available on YouTube here. (Thanks to Leigh Ann DeLyser and Dianne Levitt for this.)
• Patricia Garcia, who does terrific work on helping underserved students author their computational identities, recommends this video on Black Lives Matter myths debunked.
• The University of Michigan’s School of Information has been having an amazing online discussion about how to make their education anti-racist. A book that stood out on the resources shared there was Stamped from the Beginning: The definitive history of racist ideas in America by Ibram Kendi (Amazon link here).
• I had my children read the CACM blog post, and they gave me valuable comments on it. My daughter, Katie, a science teacher in Detroit Public Schools suggested these three books: The Color of Law: A forgotten history of how our government segregated America by Richard Rothstein (link), We Want to Do More Than Survive: Abolitionist Teaching and the Pursuit of Educational Freedom by Bettina Love (Amazon link), and Why Are All the Black Kids Sitting Together in the Cafeteria? by Beverly Daniel Tatum (Target link).

The issues of race, culture, and members of underserved groups are particularly critical for us to consider as we move into the 2020-2021 academic year during a worldwide pandemic. As we move classes on-line, we are putting at a greater disadvantage underserved populations. We have to be sensitive and thoughtful that our response to pandemic doesn’t exacerbate our existing structural inequities. Let’s worry less about cheating, and more about

• how to help students taking our classes remotely who don’t have laptops, or who have to share a single laptop with a family, or who don’t have broadband Internet access;
• how to help students who can’t come to class because they would be put at risk;
• how to help students who have hearing disabilities and won’t be able to read lips if a teacher is wearing a mask (thanks to Bonnie MacKellar for pointing out that concern).

We have privilege and resources, and we should use them to address inequities.

TL;DR: I know too little about race, and I have not considered the historic and systemic inequities in CS education when I make my daily teaching decisions. I haven’t read all of the above, but I’m working on it daily. Please do share valuable resources you have found in the comments. Let’s learn about race in CS education and make change to improve learning for everyone.

Goals for CS Education include Getting Students In the Door and Supporting Alternative Endpoints

ACM Inroads has published an essay by Scott Portnoff “A New Pedagogy to Address the Unacknowledged Failure of American Secondary CS Education” (see link here). The Inroads editors made a mistake in labeling this an “article.” It’s an opinion or editorial (op-ed) piece. Portnoff presents a single perspective with little support for his sometimes derogatory claims. I have signed a letter to the editors making this argument.

Portnoff is disparaging towards a group of scholars that I admire and learn from: Joanna Goode, Jane Margolis, and Gail Chapman. He makes comments about them like “had CSEA educators been familiar with both CS education and the literature.” Obviously, they are familiar with the research literature. They are leading scholars in the field. Portnoff chides the CSEA educators for not knowing about the “Novice Programmer Failure problem” — which is a term that I believe he invented. It does not appear in the research literature that I can find.

In this blog, I want to try to get past his bluster and aggressive rhetoric. Let’s consider his argument seriously.

In the first part, he suggests that current approaches to secondary school CS education in the United States are failing. His measure of success is success rates on the Advanced Placement Computer Science Principles exam. He also talks about going on to succeed in other CS courses and about succeeding at industry internships, but he only offers data about AP CSP.

He sees the reason for the failure of US CS education in high school is that we have de-emphasized programming. He sees programming as being critical to success in the AP exams, in future CS classes, and in industry jobs. Without an emphasis on programming, we will likely continue to see low pass rates on the AP CS Principles exam among female and under-represented minority students.

In the second part, Portnoff lays out his vision for a curriculum that would address these failings and prepare students for success. He talks about using tools like CodingBat (see link here) so that students get enough practice to develop proficiency. He wants a return to a focus on programming.

What Portnoff misses that there is not consensus around a single point of failure or a set of goals about CS Education. In general, I agree with his approach for what he’s trying to do. I value the work of the CSEA educators because the problems that they’re addressing are harder ones that need more attention.

The biggest problem in US high school CS education is that almost nobody takes it. Less than 5% of US high school students attend any CS classes (see this blog post for numbers), and the students we currently have are overwhelmingly male, white/Asian, and from wealthier schools. Of course, we want students to succeed at the Advanced Placement exams, at further CS courses, and at industry jobs. But if we can’t get students in the door, the rest of that barely matters. It’s not hard to create high-quality education only for the most prepared students. Getting diverse students in the door is a different problem than preparing students for later success.

CSEA knows more about serving students in under-served communities than I do. They know more about how to frame CS in such a way that principals will accept it and teachers will teach it. That’s a critical need. We need more of that, and we probably need a wide range of approaches that achieve those goals.

A focus on programming is critical for later success in the areas that Portnoff describes. The latest research supporting that argument comes from Joanna Goode (as I described in this blog post), one of the educators Portnoff critiques. Joanna was co-author on a paper showing that AP CS A success is more likely to predict continuation in CS than AP CSP success. I’m also swayed by the Weston et al. article showing that learning to program led to greater retention among female students in the NCWIT Aspirations awards programs (see link here).

I also agree with Portnoff that learning to program requires getting enough practice to achieve some level of automaticity. CodingBat is one good way to achieve that. But that takes a lot of motivation to keep practicing that long and hard. We achieve reading literacy because there are so many cultural incentives to read. What will it take to achieve broad-based programming literacy, and not just among the most privileged? Portnoff tells us that his experience suggests that his approach will work. I’m not convinced — I think it might work with the most motivated students. He teaches in the same school district where the ExploringCS class was born. But Portnoff teaches in one of LAUSD’s premier magnet schools, which may mean that he is seeing a different set of students.

An important goal for CS Education is to get students in the door. I’m not sure that Portnoff agrees with that goal, but I think that many involved in CS education would. There is less consensus about the desired outcomes from CS education. I don’t think that CSEA has the same definition of success that Portnoff does. They care about getting diverse students to have their first experience with computer science. They care about students developing an interest, even an affinity for computing. They care more about creating a technically-informed citizenry than producing more software developers. Portnoff doesn’t speak to whether CSEA is achieving their desired outcomes. He only compares them to his goals which are about continuing on in CS.

There is a tension between preparing students for more CS (e.g., success in advanced classes and in jobs) and engaging and recruiting students. In a National Academy study group I’m working in, we talk about the tension between professional authenticity (being true to the industry) and personal authenticity (being personally motivating). The fact that so few students enroll in CS, even when it’s available in their school, is evidence that our current approaches aren’t attractive. They are not personally authentic. We need to make progress on both fronts, but considering how over-full undergraduate CS classes are today, figuring out the recruitment problem is the greater challenge to giving everyone equitable access to CS education.

I just learned about a new paper in Constructionism 2020 from David Weintrop, Nathan Holbert, and Mike Tissenbaum (see link here) that makes this point well, better than I can here. “Considering Alternative Endpoints: An Exploration in the Space of Computing Educations” suggests that we need to think about multiple goals for computing education, and we too often focus just on the software development role:

While many national efforts tend to deploy rhetoric elevating economic concerns alongside statements about creativity and human flourishing, the programs, software, curricula, and infrastructure being designed and implemented focus heavily on providing learners with the skills, practices, and mindset of the professional software developer. We contend that computing for all efforts must take the “for all” seriously and recognize that preparing every learner for a career as a software developer is neither realistic nor desirable. Instead, those working towards the goal of universal computing education should begin to consider alternative endpoints for learners after completing computing curricula that better reflect the plurality of ways the computing is impacting their current lives and their futures.