Computing Ed Research – Guzdial's Take

My last update on the University of Michigan’s Program in Computing for the Arts and Sciences (PCAS) was in March 2024 (see post here). I did give some updates when I wrote about our SIGCSE 2025 papers (see that post here). So much has changed since 2024, and I wanted to plug our search for a new Lecturer, so here’s a brief update.

That 2024 update mentioned that we were up to 300 students enrolled in PCAS courses. We hit 700 this semester. It’s remarkable to have this kind of growth when none of our courses are required by any majors. Several of the courses meet some requirement in different majors. For example, our course COMPFOR 111: Computing’s Impact on Justice (see page describing that course here) meets the Race and Ethnicity course requirement for liberal arts and sciences students (the only computing course that meets that requirement) and it meets the ethics requirement for majors like Computer Science and Data Science.

Here’s a snapshot of our current course offerings, organized in terms of our three themes: Computing for Discovery, for Expression, and for Justice. Descriptions of all PCAS courses can be found on this website. This year, I’ve been building out COMPFOR 451: Capstone in Computing for the Arts and Sciences (course page here). It has been great fun helping students in drama, art history, communications, and English tackle computational capstone projects.

Our sections pretty much fill up these days, so our growth comes from offering more sections. We are trying to hire lecturers every year. We’re up to three now:

• Brian Miller (PhD in Music) has been with us the longest. He re-organized the Justice course so that it meets the Race and Ethnicity course requirements. He also teaches in our Expression theme.
• Donovan Colquitt (PhD in Engineering Education) is shared with the optiMize program in social innovation. He teaches the Justice course as well, which is our largest course in terms of enrollments and sections.
• James McCormick (PhD in Molecular and Cell Biology) just started with us this year. He’s a computational scientist, and is teaching courses on Python for the sciences (our second largest course) and for digital media. He’s working on expanding our offerings in Computing for Discovery which is really exciting.

I mention their degrees because I continue to be the only person teaching in PCAS with a graduate degree in Computer Science. It doesn’t have to stay that way, but I think that’s one of the keys to our early growth. Our courses are developed with and taught by experts in computational science, critical computing, computational art, and digital humanities. That drives some of the student interest. PCAS is different from a computer science department.

We are trying to hire a fourth lecturer this semester. Since we just hired James for our Discovery theme, we’d like our next lecturer to be able to teach in the Expression and Justice themes. I’d love it if they could teach Alien Anatomy (more on our course on GenAI for students across campus here). I’m teaching that again right now, and I plan to write more here about the experience — it’s fascinating to see how students are thinking about generative AI, and Snap is such a wonderful tool to use in learning about AI. We will be looking until April 10, 2026, so if you know any candidates, please send them our way — here is the link to our posting in Interfolio.

This is policy wonk stuff, but I find policy fascinating. As a researcher, it’s hard to figure out “How are most people (students, faculty, whatever) in this field thinking about X?” Policy-makers have to figure that out, too, and then have to respond. A change in policy is like a research paper that says, “We found that the status quo wasn’t working anymore.”

The English government has just conducted an independent review of all their school curricula (see report here).  The review is critical of how Computer Science is working in English schools today. They say that Computing now pervades all disciplines and “digital literacy” should be taught in an integrated manner. I recommend reading the report — it’s accessible and covers a bunch of important issues, like who is taking CS and where there’s a split between policy and practice.

One of the explicit recommendations is that the government:

Replaces GCSE Computer Science with a Computing GCSE which reflects the full breadth of the Computing curriculum and supports students to develop the digital skills they need.

The government response (linked here) agrees:

We agree with the Review that the computing curriculum should be the main vehicle for teaching about digital literacy, and we are confident that delivering the computing recommendations will provide more pupils with valuable digital skills that are essential for the future.

It is also clear that, in some subjects, digital methods now influence the content and how it is taught. We will work with experts to assess the validity of digital practice in these subjects, the evidence of whether this can be done robustly and whether it merits inclusion in the new curriculum. Where it does, we will include a requirement for the relevant digital content in those subjects’ programmes of study and we will ensure that it aligns with the computing curriculum, to reduce the risk of duplication.

We will also replace the computer science GCSE with a broader offer that reflects the entirety of the computing curriculum whilst continuing to uphold the core principles of computer science such as programming and algorithms, and explore the development of a level 3 qualification in data science and AI.

Bottomline: CS just isn’t the thing anymore. Computing and computing across the curriculum is what is needed.

As a director of a Program in Computing for the Arts and Sciences, and someone who spent 25 years in a College of Computing, I wholly endorse this change and welcome it. As I described in a blog post from a couple of years back, “computer science” was originally invented to be a broad subject to be taught to everyone. Over the last 60 years, “computer science” has become more narrow (e.g., overly emphasizing algorithms while de-emphasizing building and creativity and social impacts, as Sue Sentance describes in this blog post, while “computing” represents a broader perspective. When we think about what should be taught to everyone in secondary school, Computing (and digital literacy, as the reports suggest) are more appropriate than what we now mean when we say Computer Science.

Some of you may remember the Apple ads that emphasized the computer as a “bicycle for the mind.”

(From https://folklore.org/Bicycle.html)

GenAI is not like a bicycle for the mind. Instead, it’s more like an automobile. I’m finding that comparison to be useful in thinking about how GenAI may impact our world.

A bicycle extends our abilities. It allows us to do more with our legs and bodies than we can without the bicycle. The automobile also extends our abilities, but it doesn’t use those abilities. As Paul Kirschner recently wrote, GenAI is not cognitive offloading. It’s outsourcing. We don’t think about how to do the tasks that we ask GenAI to do. As the recent Anthropic study showed, you don’t learn about the libraries that your code uses when GenAI is generating the code for you (press release version, full ArXiv paper).

Automobiles have had an enormous impact on modern society. We can go places and do things that we couldn’t previously. Most of us can’t bike across the US, but many of us drive across it. So, we drive a lot and bicycle less. But there’s a cost — our bodies and minds atrophy if we do not use them.

Ford sold the Model-T as a general tool. (“You can have any color you want as long as it’s black.”). Users made changes to it to adapt it for various conditions and tasks. Today, those changes have led to a wide range of vehicles for a wide range of purposes: sedans, minivans, pick-up trucks, SUVs, and big rigs for hauling materials over long distances. Ford could not have anticipated all those different uses and specializations. They evolved over time.

To gain the benefit of automobiles, we made enormous changes to our infrastructure. We have freeways and driveways, garages and parking structures, gas stations and tire shops. We have changed our environment in order to use the automobile more.

But over time, we have seen those costs. Automobiles (and associated infrastructure, like blacktop parking lots) have had a large, negative impact on our ecology. Neighborhoods were destroyed when freeways were built through them.

We are starting to roll back some of our society’s earlier decisions that favored the automobile. We develop hybrid and electric cars that have less negative impacts on our ecology. In my town, bike lanes are being added, explicitly to choke automobile traffic in order to encourage more biking and less driving. We want people to use their bodies more. Exercise is an antidote to many of the automobile’s ills.

Here’s what I predict based on this comparison:

• We are going to see more specialized forms of GenAI, that we are going to have difficulty imagining today. Already, I am seeing the best learning outcomes from tools where GenAI is built into the tool (like Xu Wang’s work and Barbara Ericson’s). Chat is the Model-T of GenAI. It’ll get used for lots of purposes, but we’ll eventually figure out the specialized forms that will be much more useful.
• We are going to change our infrastructure to enable GenAI. More power plants, more power distribution, more data centers. Eventually, we’ll figure out that we went too far, and we’ll scale those back. But right now, it’s hard to estimate what’s “too far.”
• We will likely overuse GenAI and some of our abilities will atrophy, without a lot of self-regulation and careful consideration of how we use GenAI. Generative AI is a marshmallow test. We will have to figure out that we need to exercise our minds, even if GenAI could do it easier, faster, and in some cases, better.

I have written several blog posts about the different ways to implement Media Computation in introductory programming courses. We built JES at Georgia Tech in 2002 and the final release was in 2020. Our introductory course in PCAS that uses Media Computation, COMPFOR 121: Computing for Creative Expression, uses Snap! and Pixel Equations (as described in this blog post). Our Python course (COMPFOR 221: Digital Media with Python) started in Python3 with the JES4Py library, but then we moved to Google Collaboratory notebooks (the libraries for that are available here).

Dave Largent at Ball State continues to teach Media Computation. The students in his course compete in an art show each term for which I’ve served as a judge. Dave let me know that he and his students have extended JES4Py and have released a new library:

I’ve had a couple of undergrad students working with me to redevelop/extend Gordon College’s JES4py package. We’ve published it at PyPI under the name mediaComp (https://pypi.org/project/mediaComp/). Our GitHub is https://github.com/dllargent/mediaComp.

Why is this interesting? Why is anybody teaching with a 20 year old method, and even making new libraries for it?

Maybe because it answers a CS education need that has only grown more important. Data science is a bigger deal now than it was 20 years ago. Ben Shapiro told us in 2018 that machine learning was going to change the CS curriculum, and we needed to think more about data. But what data are interesting to students?

The empirical studies in computing education research are pretty clear: Motivation matters.. Students get frightened off by computer science classes. They find our examples boring. If we can teach the same computing concepts using any data, why not use data that students find interesting?

I’ve heard Jens Mönig, lead architect and developer on Snap!, answer this question several times in several talks. There’s a new interview with him in the most recent ACM Inroads magazine (link) with Jens where he makes the point again. Students are interested in their data. Personal data, data about them, data that they make, data that are relevant to them. The phrase in the Constructionist community is “personally meaningful.”

Media Computation is data manipulation with personally meaningful data — your pictures and sounds, or the pictures and sounds that interest you. There are a lot of pixels and samples in those pictures and sounds. Those are data that matter to the students who care about those pictures and sounds.

Media Computation as an approach is not going to be for everyone. But every computing teacher should answer the meta question, “Why should my students care about these data?”. We often use the Corgis project data to help students find data that are personally meaningful. That’s where you can find the Titanic passenger dataset that Jens talks about in his interview. I am an advisor to API Can Code, which is a curriculum all about doing data science with live data that students might care about.

My point here isn’t that all teachers should use Media Computation. My point is that all computing teachers should engage students with personally meaningful data.

I’ve reached the stage in my career where I’m attending conferences not because I’m presenting a paper but because I’ve agreed to take on a service role.. That’s not a bad progression. I won’t be at ACM SIGCSE 2026 because I have neither a paper not a service role — I’ll miss seeing everyone, and hope you enjoy St. Louis.

I will be at ACM ITiCSE 2026 and 2027, serving as co-chair for the Doctoral Consortium. Please, PhD students, come join Monica Divitini from NTNU and me at this year’s DC in Madrid. More information on the DC and how to apply is here.

I will also be at ACM ICER 2026 and 2027, serving as co-chair for lightning talks and posters. I’ll pester you with that Call for Participation when the page gets posted.

My planned activity during my sabbatical was to revise my 2015 book “Learner-Centered Design of Computing Education.” One of the fixes I wanted to make was a better definition of what “learner-centered design” was. In the new edition, I wrote some formal defining stuff, and then I wrote the below — an extended metaphor to make distinctions between different kinds of “centering” in education. I’m sharing that section here (in its pre-reviewed and pre-edited state). It comes right after defining what the Zone of Proximal Development is and what student performance means.

There are many different kinds of teaching activity that can help a student reach a more sophisticated level of performance. A teacher can model successful performance. The teacher can give feedback on the student’s performance. The teacher can coach or guide a student while attempting a task. They can set expectations in the class which create a social context for success. They can use teaching methods that have a proven research record in promoting engagement and student performance.

Figure 1: A metaphor for teaching contrasting learner-centered and standards-centered

Consider teaching from the top or bottom of the ZPD. Here is a metaphor to make distinctions between two kinds of support in order to create a geography of teaching. Imagine the ZPD as a climbing wall (Figure 1). The student is at the bottom and wants to reach the top. Depicted as grayscale images in this figure, here are two ways a teacher might support the student in scaling this wall:

• The supporter at the bottom can help the student get started, giving them a “boost” or “leg up.”
• The supporter at the top can reach down, and get them the rest of the way to the top of the wall.

The supporter at the bottom is more flexible than the one at the top. She can move to where the student is actually standing. She can help the student scale different parts of the wall or even reach different goals along the wall. She can bend even further if the student is shorter.

But a disadvantage for the supporter at the bottom is that she cannot be absolutely sure that the learner reaches the top. She can meet the student where they are when they first face the wall. She can help them get started on whatever path they choose on the wall.

The supporter at the top can help students who are almost at the top of the wall. He can be sure that students actually reach the learning objective. When he is reaching down, he is in a fixed position. He can help the student reach the objective where he is at, the level that he has already achieved. He can also be sure when a student does not reach this standard – he can see the students who fall, or who do not make it to his level. He is in a better position to decide whether the student is going to achieve the desired objectives.

The supporter at the bottom is more learner-centered. The supporter at the top is more standards-centered. Neither supporter is particularly strong at helping the student in the middle, when the student is challenged to persist, to stay engaged, and to maintain motivation. If the student is not particularly interested in achieving the top of the wall, they are satisfied making it part-way to the objective, then the learner-centered teacher has the most to offer.

Learner-centered teaching is concerned with helping students where they are, helping them to get started, and getting them engaged and motivated to tackle the mid-part. Low enrollment and high withdrawal or failure rates (sometimes called WDF rates) are issues that learner-centered teaching addresses. Learner-centered teaching also addresses issues of diversity, with the goal that all kinds of students can succeed in the class — even those who think that they cannot succeed or do not have the prior background to succeed.

Standards-centered teaching is concerned about making sure that students have what they need to go on, in their studies or in their career. Students who fail the second class because they did not learn enough in the first class is an issue for standards-centered teaching. Talking to industry partners about the desired out- comes is standards-centered. Concern about what graduates can do and achieve is a standards-centered teaching issue.

(I’m skipping some text here about teacher-centered, classroom-centered, and other forms of structuring education.)

I am splitting hairs a bit between child-centered and learner-centered. Learner-centered also starts from the students’ interests and considers the learner’s needs, and is very much about student construction of knowledge in their own minds, since that is how learning takes place. As described in Chapter 2, the knowledge to be learned in learner-centered education is defined by the community of practice. That is external to the learner.

Within the metaphor, I am describing three kinds of teaching: Learner-centered (supporter at the bottom), standards-centered (supporter at the top), and maintaining motivation and engagement (in the middle). Of course, teachers and students have to address all these issues, but it is sometimes useful to focus on one part. Consider this metaphor: If you have heart problems, it is important to go to a cardiovascular specialist. That does not mean that you do not need to care about skeleton, digestion, and skin; you need all of those, but sometimes you can address critical issues or fix problems by specializing. I focus on the first one because it is the most important. I like the way my colleagues Amy Bruckman and Betsy diSalvo put it

Computer science is not that difficult, but wanting to learn it is.

During our sabbatical, Barb and I spent a week in Auckland. We gave talks at the Auckland University of Technology and University of Auckland. Alison Clear (past Chair of the SIGCSE Board) and Tony Clear hosted us in their home, which was made even more delightful by Cary Laxer and his wife visiting. Alison organized a picnic with Paul Denny and Andrew Luxton-Reilly and their families. Tony hosted us at AUT, and Paul hosted us at the University of Auckland. It was a wonderful experience. If you ever get the chance to try Alison’s cooking, you have to take it. She’s amazing.

We got the chance to talk to Tony about some of his recent columns in Inroads magazine. I admit that when I get a new issue of Inroads or Communications of the ACM, I skim the table of contents for new feature articles. I usually skip the columns. After talking with Tony about his recent columns, I realized that I was missing out.

Tony writes from his perspective as a New Zealand scholar. It’s different than the average American perspective. The experiences and values lead to different questions and concerns.

We talked with him a good bit about his piece: “Large Language Models, the ‘Doctrine of Discovery’ and ‘Terra Nullius’ Declared Again?”. I didn’t know that the European colonists who came to Australia and New Zealand had papal permission. Australia was declared terra nullius — nobody owned the place, so go ahead and take it over. New Zealand was recognized as being run by the Māori, so colonizing there had to be negotiated. Tony asks, “So which of these models gives LLMs the right to consume the Internet?” Do we assume that nobody owns all the content on the Internet (like Australia)? Or should we be negotiating rights? The idea of LLM providers as a colonizing force was a fascinating perspective.

We also talked about his column that was published after we left: Project Carbon Budgeting. People in New Zealand were much more critical than in the US about data centers relying on nuclear power. New Zealand is a nuclear-free zone. They decided that the benefits are not worth the risks. Tony’s position is less critical about the LLMs themselves than he is about our job as educators. LLMs are having a huge impact on CS education, but we are not talking enough about the ethics of their use — from energy demands to ecological impacts. It’s our job to raise these issues in our classes. In New Zealand, the fact that GenAI providers are nuclear-powered is a critical issue. CS educators should be talking about that.

I’ve been fortunate to know Tony for a long time. We have had lots of research discussions. We have served as mentors at the same Doctoral Consortia. It wasn’t until I was talking with Tony about his columns, after I’d already been living in New Zealand for several weeks, that I became attuned to the New Zealand perspectives that he was bringing to his columns. That’s entirely on me — I wasn’t paying enough attention.

But that’s made me think about where else we make assumptions about a shared perspective when there’s actually an important difference that helps to see situations in a new light. Alan Kay famously said, “A change in perspective is worth 80 IQ points.” A quote (often attributed to McLuhan and his students, but is actually older), “I don’t know who discovered water, but it wasn’t a fish.” If you don’t see a problem from other perspectives, you may not be seeing the problem at all.

Last month, my daughter was married in Indore, India. It was beautiful — an amazing set of ceremonies over several days. This trip was the most time I have spent in India, and the most I have traveled there. It was such a radically different context than my life as an American professor in a college town. When I came back to the new term, I was immersed in the on-going discussions about GenAI in our classes, about how AI is going to take everyone’s jobs, and on how we should start planning for a “post-labor” society. I understand why the people in my daily context are worried about AI. I’m not sure that it’s the same for people I met and interacted with in India. Will GenAI be changing the real estate business all that much? Construction? Being a travel guide for foreigners? Tailoring clothes? Driving an auto-rickshaw? Or even driving at all? I’d never trust an autonomous vehicle trained in the US on the streets in the Indian cities I visited. I know that I saw only a small slice of India, but even that small slice gave me a different perspective than my daily life. GenAI is going to change a lot, but maybe we overestimate the impact because of the bubbles we live in.

ACM SIGCSE now has the ACM Global Computing Education Conference (CompEd), held this last October in Botswana. I hope that this conference will help all of us see our CS education problems and issues in new perspectives. Tony helped me see the New Zealand perspective in his columns. My time in India gave me new insight into the US-centrism of the AI discussions I’m part of. We could use those additional 80 IQ points.

“You can observe a lot by just watching.” – Yogi Berra

I had a couple of amazing experiences that made me think about how little we see each other teach and how much can be gained from doing it more.

We’re challenged to scale PCAS when we don’t have an undergraduate major nor a graduate program. For us to hire undergrad or graduate teaching assistants means we have to recruit from other majors and departments. CS and Information majors often don’t work out, in part because it’s a foreign idea for them to learn about computing and programming for a purpose other than getting a Tech job.

Last Fall, we discovered that the Math Department had PhD students who are former K-12 math teachers. They care about teaching, they’re trained as teachers, and the Math Department can’t employ them all. They are amazingly motivated graduate students. They gave up paying gigs to become students. When we lose them as teaching assistants, we typically lose them to research positions.

Katie Waddel was the first math PhD student with whom I worked. She worked with me on “Digital Media with Python.” She was much more a co-teacher than a teaching assistant. She came to every lecture. She had great ideas for improving the course structure. Like coming up a with a classroom seating rotation sure that the quiet kids would get the chance to sit near the more talkative kids for better discussions.

And sometimes — ever so kindly — she gave me notes on my teaching. She’d point out where some group of students wasn’t getting something, and we’d talk about how to change what I was teaching. Or she’d tell me when I was getting too geeky, and we’d talk about better ways of explaining the technical content.

I’ve been teaching a long time — it’s been 45 years since I taught my first programming course. I have team-taught maybe a dozen times. have rarely had someone try to make me teach better. It was a great experience that I heartily recommend.

From February to May of 2025, my wife, Barbara Ericson, and I taught at the University of Canterbury in Christchurch, New Zealand. We were on sabbatical (my first!). It was an amazing experience. I got to team-teach with Tim Bell. Tim is the inventor of CS Unplugged, ACM Karlstrom Award winner, and SIGCSE Outstanding Contributions award winner — and a keyboardist (we heard him perform several times while we were there).

Probably a surprise to no one, Tim is a master teacher. He’s inventive, funny, and deeply interested in the students and their learning. I co-taught with him in a course he’s taught for 30 years, an introductory course on programming and data literacy. We had about 100 students. I sat in on all his lectures during the three months we were there.

How often have you watched someone teach? Especially someone whose expertise is education and who has been honing this course for decades? It’s different than watching a TED talk, or a keynote, or a recorded one hour lecture. I saw Tim plan the course, connect the pieces across multiple weeks, and invent new things — in a course that he’d taught dozens of times. That’s hard to do. We all develop inertia in what and how we teach.

It was such a privilege to watch Tim at work. My notes on the class are filled with bits and pieces that stuck with me. Tim made the Internet real by showing pictures of where the undersea Internet cables came ashore in New Zealand. I learned a new way to explain the Nyquist Theorem. I have always kind of ignored floating point notation as being too complicated, but Tim had this terrific binary simulator that helped me to understand what “1.101” means in binary.

I don’t think I’m saying anything here that anyone would disagree with. Of course, we would be better teachers if we had an experienced teacher watch us teach and give us tips. Of course, we would learn a lot about teaching if we could co-teach with a master teacher.

So why don’t we this?

One big reason is economic —- it’s more expensive to pay for two teachers to be in the classroom than to pay for just one. So, we only assign one teacher to a classroom, and that teacher teaches alone.

We could work around the cost problem. We find ways to pay for things that are important for us. At my University, there is a huge staff to promote research, to manage grants, and even to help write grants. All of that research infrastructure costs far more than adding another teacher to a classroom.

But here’s a bigger reason. When was the last time that you did something to improve how you teach? K-12 teachers can probably give concrete answers to that question. It’s part of their practice to continually improve. University teachers are less likely to engage in professional teaching development —- and that’s a shame. We can always get better at any practice. I’m at this for 45 years now, and I’m still working at getting better at it. We show value for the practice by taking our development in that practice seriously.

It’s hard to make professional teaching development happen. But in my experience, it’s worth making happen. I recommend that you find ways to get excellent teachers to watch you teach. I recommend finding ways to team-teach with excellent teachers — and actually watch one another teacher, and try to make each other better. Find ways to make your teaching better, and a great way to do that is by working with other teachers.

In May, Tamara Nelson-Fromm defended her dissertation “A Qualitative Exploration of Programming Instruction for Alternative Endpoints in Post-Secondary Computing Education.”

I’ve talked about Tamara’s work a few times in this blog.

• One of her early projects was a teaspoon language to help history teachers to build history timelines (blog post).
• At PLATEAU 2024, she presented our paper suggesting that there was transfer from the Pixel Equations teaspoon language into building Image filters in Snap! (blog post).
• She presented our paper at SIGCSE 2025 on how we designed the PCAS courses oriented towards creative expression and social justice (blog post). Tamara worked with me on that design process, particularly on how to meet justice scholars desire for their students to learn about databases, HTML, and SQL (blog post) and on helping students to understand how a computer might generate language (blog post).
  

Tamara has published a lot more than that during her PhD work in part because she became an expert on reflexive thematic analysis. She worked with several other students on using RTA. At SIGCSE 2026, she and Aadarsh Padiyath will present their paper on how to use RTA for computing education research. I’ve read the paper and loved it — I have been recommending it widely.

Tamara with her committee: Valerie Barr (on Zoom), (from right) Nikola Banovic, Barry Fishman, Tamara, and me

I want to tell you about her dissertation, but I don’t want to divulge too much — only the first study has been published so-far. The big idea that drives her work is alternative endpoints. She and I have talked a lot about the paper by Mike Tissenbaum and his colleagues. The big question that she’s helping to answer is “What will CS education look like as we move beyond producing more software developers?”

Study #1: New CS Teachers learning Debugging: Her first study investigated how we develop new CS teachers. From the start of her PhD, she has been interested in how students learn to debug. Her method was novel (and hard to get past reviewers). Instead of studying new CS teachers and how they learned debugging, she interviewed expert teachers of new CS teachers. She interviewed the people who run professional training, summer workshops, and many the other ways that teachers learn CS. Rather than track individuals (who might not struggle with debugging, or who might not be representative of new teachers), she talked to people who have been doing this for years. What do they do to teach debugging?

Here was the amazing answer: Avoid it. In hindsight, it makes all the sense in the world. Imagine: You’ve got a teacher new to CS in your workshop. In the first workshops (which is often all you get with teachers), you want them to succeed.. You want them to come back for more workshops. So, you do all that you can to avoid bugs. Since bugs will happen still, you provide checklists and “Here’s what to look for if it doesn’t work” guidance.

Of course, really learning to debug comes later…or does it? Tamara raises the intriguing possibility that maybe that’s enough. Maybe for what these teachers are doing (especially in primary school), maybe it’s enough to just have checklists. Again, it’s about alternative endpoints — what does a K-12 teacher need to know about debugging? The paper on her first study will appear at SIGCSE 2026 in February.

Study #2 and #3: PCAS Students: Her second and third studies involved PCAS students. In her second study, she looked at why arts, sciences, and humanities students would want to take courses involving programming. In her third study, she returned to the theme of the first study — how do PCAS students debug?

I don’t want to say too much about these studies, but I do want to tell one story from Study #3 that connects strongly to the story about teachers in Study #1. One of the ways that Tamara saw PCAS students debugging was the way that your modern mechanic fixes your car.

Mechanics today do not need to how your car actually works. Instead, they plug it into the diagnostic machine, and they get a code. The code tells the mechanic where the problem is. The mechanic then follows a procedure or (more likely) replaces a part — whatever the manufacturer guidance is for that code. They then try it again.

That’s how some of the PCAS students debugged. Each assignment for the arts and humanities classes was open-ended, and I gave them completely working examples. The students would write their programs and try them. If they didn’t work, they’d check that they didn’t make a simple mistake. If they couldn’t figure it out, they would go back to one of the worked examples and copy-paste the part that worked and did about the same thing. Then they’d test again. If they still couldn’t get it to work, they’d explore changing what they were trying to do, so that they still met the requirements — but they could get it working.

Is this problem? Do the students need to learn better debugging skills? Let’s go back to alternative endpoints again. Not everyone needs to have a strong mental model of the working program.

Tamara wasn’t prescriptive in her dissertation. She didn’t make judgements of good or bad. Rather, she described the world as she found it, and raises the reasonable possibility that what she saw is working just fine.

Tamara’s dissertation is important. The alternative endpoints paper suggested that we should think about different audiences learning to program for different purposes than software development. Tamara showed us what that is looking like.

I stopped blogging while I was on sabbatical because I had to focus on finishing the second edition of Learner-Centered Design of Computing Education. And then we came back from sabbatical. I’d heard that it was tough getting back to normal work after sabbatical, and it was. I had it easier than most (e.g., I came back to summer time, and I had a light teaching schedule this Fall). But it was still a transition, so it’s taken me awhile to get back to blogging.

In the meantime, Aadarsh Padiyath published two papers (and a poster) about the the development and validation of an instrument to measure Critical Reflection and Agency in Computing. Aadarsh Padiyath is a PhD student (soon to graduate! Hire him!) advised by Barb Ericson and me. He last appeared here with a guest post a year ago with a pushback against technological determinism — computing education researchers assuming that the future of CS education can be predicted by the development of ChatGPT.

These new papers are about the second study from his dissertation. Aadarsh is interested in how we can better prepare computer sciences for recognizing and dealing with ethical issues. Typically, we do that with computing ethics classes. But do they work? Aadarsh recognizes that being able to measure progress is an important way to encourage progress.

In May, he published a paper at CHI 2025 “Development of the Critical Reflection and Agency in Computing Index.” The title captures the two aspects of computing and ethics that Aadarsh is most interested in — that student reflect on the ethical implications of their work and that they have a sense of agency, i.e., that they can do something that can address problems. This first paper was about defining the constructs (see Table 1 below). He created 45 items for his measure. He had a panel of experts review the items, and he interviewed five undergraduate students as they responded to the items. His paper was recognized with a Best Paper Honorable Mention.

Aadarsh presented a poster at SIGCSE 2025 in February, “The Development and Validation of the Critical Reflection and Agency in Computing Scale.”

The big finale was his ICER 2025 paper in August, “Validation of the Critical Reflection and Agency in Computing Index: Do Computing Ethics Courses Make a Difference?”. This paper summarized the CHI 2025 story of how the index came to be, then presented the results of a two-round validation study (474 participants in one, 464 in the other). Overall, he has strong support for the validity of his measure.

But in addition to taking the measure, Aadarsh ask the participants if they had taken a computing ethics course. He found “Participants who completed computing ethics courses showed higher scores in some dimensions of ethical reflection and agency, but they also exhibited stronger techno-solutionist beliefs, highlighting a challenge in current pedagogy.” Here’s my interpretation of his results: after taking a course in computing ethics, students were more reflective (yay!) and believed that they could make a change if they saw an ethical problem in their work (double yay!), but they tended to belief that more technology is the answer to addressing ethical problems with technology (uh-oh).

This is an impressive set of papers. It gives us a way of measuring the impact of our interventions in teaching computing students about ethics. It also highlights some real issues that we should be addressing in our computing ethics classes.