Updates: Workshop on Contextualized Approaches to Introduction to Computing, from the Center for Inclusive Computing at Northeastern University

From Nov 2020 to Nov 2021, I was a Technical Consultant for the Center for Inclusive Computing at Northeastern University, directed by Carla Brodley. (Website here.) CIC works directly with CS departments to create significant improvements in female participation in computer science programs. I’m no longer in the TC role, but I’m still working with CIC and Carla. I’ll be participating in a workshop that they’re running on Monday March 21. I’ll be talking about Media Computation in Python, and probably show some of the things we’re working on for the new classes here at Michigan.

https://www.khoury.northeastern.edu/event/contextual-approaches-to-introduction-to-computing/

Contextual Approaches to Introduction to Computing

Monday 3/21/22, 3pmEST/12pmPST

Moderator: Carla Brodley;  Speakers: Valerie Barr, Mark Guzdial, Ben Hescott, Ran Libeskind-Hadas, Jakita Thomas

Brought to you by the Center for Inclusive Computing at Northeastern University

 

In this 1.5 hour virtual workshop, faculty from five different universities in the U.S. will present their approach to creating and offering an introductory computer science class (CS0 or CS1) for students with no prior exposure to computing. The key differentiator of these approaches is that the introduction is contextualized in one area outside of computing throughout the semester. Using the context of areas such as cooking, business, biology, media arts, and digital humanities, these courses appeal to students across the university and have realized spectacular results for student retention in CS0/CS1, persistence to taking additional CS courses, and declaring a major or minor in computing. The importance of attracting students to computing after they enter university is critical to moving the needle on increasing the demographic diversity of students who graduate in computing. Interdisciplinary introductory computing classes provide a pathway to students discovering and enjoying computing after they start university. They also help students with no prior coding experience gain familiarity with computing before taking additional courses required for the CS major. The workshop will begin with a short presentation by each faculty member on their approach to contextualized CS0/CS1 and will touch upon the university politics involved in its creation, the curriculum, and the outcomes. We will then split into smaller breakout sessions five times to enable participants to meet with each of the five presenters for questions and more in-depth conversations.

Updates: Developing the University of Michigan LSA Program in Computing for the Arts and Science

This blog is pretty old. I started it in June 2009 — almost 13 years ago. The pace of posting has varied from every day (today, I can’t understand how I ever did that!) to once every couple of months (most recently). There are things happening around here that are worth sharing and might be valuable to some readers, but I’m not finding much time to write. So, the posts the rest of this week will be quick updates with links for more information.

During most of the pandemic, I co-chaired (with Gus Evrard, a Physics professor and computational cosmologist) the Computing Education Task Force (website) for the University of Michigan’s College of Literature, Science, and the Arts (LSA). LSA is huge — about 20K students. (I blogged about this effort in April of last year.) Our job was to figure out what LSA was doing in computing education, and what else was needed. Back in November, I talked here about the three themes that we identified as computing education in LSA:

• Computing for Discovery: Think computational science, or data science + modeling and simulation.
• Computing for Expression: Think chatbots to Pixar to social media to Media Computation.
• Computing for Justice: Think critical computing and everything that C.P. Snow and Peter Naur warned us about computing back in the 1960’s.
  

Our report was released last month. You can see the release statement here, and the full report here. It’s a big report, covering dozens of interviews, a hundred survey responses, and a huge effort searching over syllabi and course descriptions to find where computing is in LSA. We made recommendations about creating a new program, new courses, new majors and minors, and coordinating computing education across LSA.

Now, we’re in the next phase — acting on the recommendations. LSA bought me out of my teaching for this semester, and it’s my full-time job to define a computing education program for LSA and to create the first courses in the program. We’re calling it the Program for Computing in the Arts and Science (PCAS). I’m designing courses for the Computing for Expression and Computing for Justice themes, in an active dialogue (drawing on the participatory design methods I learned from Betsy DiSalvo) with advisors from across LSA. (There are courses in LSA that can serve as introductions to the Computing for Discovery theme, and Gus is leading the effort to coordinate them.) The plan is to put up the program this summer, and I’ll start teaching the new courses in the Fall.

Social Studies Teachers using Programming for Data Visualization: An FIE 2020 Paper Preview

The Frontiers in Education (FIE) 2020 conference starts Wednesday October 21 in Uppsala, Sweden — see program here. My student Bahare Naimipour will be presenting our paper “Engaging Pre-Service Teachers in Front-End Design: Developing Technology for a Social Studies Classroom” (see preprint here) by Bahare, me, and Tammy Shreiner. This work came long before the NSF work that we just got funded for (see blog post here), but it’s in the same line of research.

The paper is about two of our participatory design sessions with pre-service social studies teachers in Tammy’s class on data literacy. In both of these sessions, we asked teachers to program in JavaScript or Vega-Lite to build a visualization, and in the second one, we also introduce CODAP, a visualization tool explicitly designed for middle and high school students. The paper is less about the technology and more about what the teachers told us about what they thought about tools for visualization in their class.

Social studies teachers are such an interesting group to study. They’re not particularly interested in STEM, data, or computers. They want to teach social studies. Very few of our participants had ever seen any code. (One told us, “This looks a lot like setting up my MySpace page in middle school!”)They’re only interested if we can help them teach what they want to teach. It’s a hard audience to engage, in all the right ways.

I’m going to highlight just two lessons we learned here:

First: The results from the two participatory design sessions were remarkably different. Participatory design isn’t a “okay, we did that — check off the box” methodology. Each group of participants can be remarkably different. There’s no generalization here. Each session is useful, but I don’t know how many sessions we’d have to do to get anywhere near saturation. That’s okay — we learned design lessons from each session.

Second: There is no one answer to how teachers think about programming. I have heard from many people that teachers find programming hard (see this CACM Blog post about that discussion), and I’ve hypothesized that to be true in this blog (see this post). So, now I’ve been in the room as social studies teachers have their first programming experiences and interviewing them afterwards, and….it’s complicated.

Teachers tell us often in our sessions that programming is overwhelming, but several teachers also told us that CODAP (explicitly designed for their use, and not a programming tool) was overwhelming. The question is whether it’s worth the complexity — and for whom. We get contradictory responses from the teachers, which we report in this paper. One told us that she wanted a simpler tool for herself and JavaScript for her students: “I don’t mind keeping life simple for me, but I wanted to challenge my students and give them useful, new skills.” Another teacher told us the opposite: “I would like Java[script] because it would let me do more to the visualization. Vega-lite would be better for students because it seems far more simple.”

We couldn’t fit in all the great stories and insights from these two participatory design sessions. Like the teacher who wants JavaScript in her class because, “That’s similar to what they use in math and science, right? I don’t want history to be the ‘dumbed-down’ programming.” I found that surprising, and wondered what the teachers would think of a block-based language. Another teacher told us that she wants to use programming in her history class, “Because maybe that would make history ‘cool.’” One of the tensions I found most interesting in these sessions was between the desire to know the tools and be comfortable in front of the class, and the desire to push their students to learn more. Some teachers told us that they preferred CODAP to any programming tool because they would be embarrassed to get a syntax error in front of their kids, which they realized would always be possible when programming. Other teachers told us that they were more concerned with going beyond basic tools — (paraphrasing one comment we received), “My students will already know Excel and Google Sheets. I want them to do more in my class.”

Our work is ramping up now. We had another PD session with pre-service teachers in March, just before pandemic lockdown, which was our first one with our data visualization tool in the mix. We’ve just held our first workshop in August for in-service (practicing) teachers. We’ve got more workshops planned over the next year. You’ll likely be hearing more from these studies in future posts.

Proposal #1 to Change CS Education to Reduce Inequity: Teach computer science to advantage the students with less computing background

This is my second post in a series about how we have to change how we teach CS education to reduce inequity. I started this series with this post, making an argument based on race, but might also be made in terms of the pandemic. We have to change how we teach CS this year.

The series has several inspirations, but the concrete one that I want to reference back to each week is the statement from the University of Maryland’s CS department:

Creating a task force within the Education Committee for a full review of the computer science curriculum to ensure that classes are structured such that students starting out with less computing background can succeed, as well as reorienting the department teaching culture towards a growth mindset

We as individual computing teachers make choices that influence whether students with less computing background can succeed. I often see choices being made that encourage the most capable students, but at the cost of the least prepared students. Part of this is because we see ourselves as preparing students for top software engineering jobs. The questions that get asked on technical interviews explicitly drive how many CS departments teach algorithms and theory. We want to encourage “excellence.” But whose excellence do we care about? Is Silicon Valley entrepreneurial perspectives the only ones that matter? The goal of “becoming a great software engineer” does not consider alternative endpoints for computing education (see post here). Not all our students want those kinds of jobs. Many of our students are much more interested in giving back to their community, rather than take the Silicon Valley jobs that our programs aim for (see post here).

Please don’t teach students as if they are you. First, you (as a CS teacher, as someone who reads this blog) are wildly different than our normal student. Second, your memories of how you learned and what worked for you are likely wrong. Humans are terrible at reconstructing how their memory was at a prior time and what led to their learning. That’s why we need research.

In this post, I will identify four of the methods that are differential, that advantage the students with less computing background — there are many more:

• Use Peer Instruction
• Explain connection to community values
• Use Parsons Problems
• Use subgoal labeling
  

Use Peer Instruction

When I talk to computer science teachers about peer instruction and how powerful it is for learning, the most common response is, “Oh, we already do that.” When I press them, they tell me that they “have class discussions” or “use undergraduate teaching assistants.” Nope, that’s not peer instruction.

Peer instruction (PI) is a technical term meaning a very specific protocol. Digital Promise and UTeach are creating a set of CS teaching micro credentials, and the one that they have on PI defines it well (see link here). PI is where the teacher poses a question for the class for individual responses, students discuss their answers, students respond again, and the teacher reveals the answer and explains the answer. The evidence suggesting that PI really works is overwhelming, and it can be used in any CS class — see http://peerinstruction4cs.com/ for more information on how to do it. I use it regularly in Senior-level undergraduate courses and graduate courses. There are ways to do PI when teaching remotely, as I talked about in this post.

I’m highlighting PI because the evidence suggests that it has a differential impact (see study here). It doesn’t hurt the top students, but it reduces failure rate (measured in multiple CS courses) for students with less background (see paper here). That’s exactly what we’re looking for in this series — how do we improve the odds of success for students who are not in the most privileged groups.

Explain connection to community values

I blogged last year about a paper (see post here) that showed female, Black, Latino/Latina, and first-generation students take CS because they want to help society. These students often do not see a connection between what’s being taught in CS classes and what they want. That’s because we often teach to prepare students for top software engineering jobs — it’s a mismatch between our goals and their goals.

I don’t know if this is an issue in upper-level classes. Maybe students in upper-level classes have already figured out how CS connects to their goals and values. Or maybe we have already filtered out the CS students who care about community values by the upper-level and graduate courses.

CS can certainly be used to advance social goals and community values. Teach that. In every CS class, for everything you teach, explain concretely how this concept or skill could be used to advance social good, cultural relevance, and community values. If you can’t, ask yourself why you’re teaching this concept or skill. If it’s just to promote a Silicon Valley jobs program, consider dropping it. We are all revising our classes this summer for fall. It’s a good time to do this review and update.

Use Parsons Problems

Parsons problems (sometimes referred to as “mixed-up code problems”) are where students are given a programming problem, and given all the lines of code to solve the problem, but the lines are scrambled (I usually say “on refrigerator magnets”). The challenge is to assemble the correct program. My wife, Barbara Ericson, did her dissertation work (see post here) showing that Parsons problems were effective (led to the same learning as writing the programs from scratch or from debugging programs) and efficient (low time cost, low cognitive load). She also invented dynamically adaptive Parsons problems which are even better (for effectiveness and efficiency) than traditional Parsons problems.

Parsons problems work on-line, so they fit into remote teaching easily. I’ve been doing paper-based (and Canvas-based) Parsons for exams and quizzes for several years now (see post here). Parsons problems work great in lower-level classes. There is relatively little research on using them in upper-level and graduate courses — I suspect that they could be useful, if only to break up the all-coding-all-the-time framing of CS classes.

I’m highlighting Parsons problems for two reasons.

• First, they’re efficient. As Manuel noted (as I quoted in my Blog@CACM post), BIPOC students are much more likely to be time-stressed than more privileged students. I’m reading Grading for Equity by Joe Feldman which makes this point in more detail (see website). Our less-privileged students need us to find ways to teach them efficiently. This is going to be a particularly concern during a pandemic when students will have more time constraints, especially if they, or a relative, or someone they live becomes ill.
• Second, they are a more careful and finer-grained assessment tool (see this post). If you ask students with less ability to write a piece of code, you might get students who only get part of the code working, but you get little data from students who only knew how to write part of the code but get none of it working. Parsons problems help the students with less computing background to show what they do know, and to help the teacher figure out what they don’t know how to write yet.
  

Use subgoal labelling

Subgoal labelling is pretty amazing (see Wikipedia page). Even our first experiment with subgoal labelling for CS worked examples (see post here) has shown improvements in learning (measured immediately after instruction), retention (measured a week later), and transfer (student success on a new task without instruction). Since then, Lauren Margulieux, Briana Morrison, and Adrienne Decker have published a slew of great results.

The one that makes it on this list is their most recent finding (see post here). Subgoal labeling in an introductory computing course, compared to one not using subgoal labeling, led to reduced drop or failure rates. That’s a differential benefit. There was not a statistically significant improvement on learning (measured in terms of exam scores), but it kept the students most at risk of failing or dropping out in the course. That’s teaching to advantage the students with less background in computing. We don’t know if it works for upper-level or graduate classes — my hypothesis is that it would.

Data science as a path to integrate computing into K-12 schools and achieve CS for All

My colleague Betsy DiSalvo is part of the team that just released Beats Empire, an educational game for assessing what students understand about middle school computer science and data science https://info.beatsempire.org The game was designed by researchers from Teachers College, Columbia University; Georgia Tech; University of Wisconsin, Madison; SRI International; Digital Promise; and Filament Games in concert with the NYC Dept. of Education. Beats Empire is totally free; it has already won game design awards, and it is currently in use by thousands of students. Jeremy Roschelle was a consultant on the game and he just wrote a CACM Blog post about the reasoning behind the game (see link here).

Beats Empire is an example of an important development in the effort to help more students get the opportunity to participate in computing education. Few students are taking CS classes, even when they’re offered — less than 5% in every state for whom I’ve seen data (see blog post here). If we want students to see and use computing, we’ll need to put them in other classes. Data science fits in well with other classes, especially social studies classes. Bootstrap: Data Science (see link here) is another example of a computing-rich data science curriculum that could fit into a social studies class.

Social studies is where we can reach the more diverse student populations who are not in our CS classes. I’ve written here about my work developing data visualization tools for history classes. For a recent NSF proposal, I looked up the exam participation in the two Advanced Placement exams in computer science (CS Principles and CS A) vs the two AP exams in history (US history and World history). AP CS Principles was 32% female, and AP CS A was 24% female in 2019. In contrast, AP US History was 55% female and AP World History was 56% female. Five times as many Black students took the AP US History exam as took the AP CS Principles exam. Fourteen times as many Hispanic students took the AP US History exam as took the AP CS Principles exam.

Data science may be key to providing CS for All in schools.

When computer science has to be a requirement if we want it to be available to everyone

Robert Sedgewick had an essay published at Inside Higher Ed last month on Should Computer Science Be Required? (see link here). He has some excellent reasons for why students should study computer science. Several of them overlap with reasons I’ve suggested (see blog post here). He writes:

Programming is an intellectually satisfying experience, and certainly useful, but computer science is about much more than just programming. The understanding of what we can and cannot do with computation is arguably the most important intellectual achievement of the past century, and it has led directly to the development of the computational infrastructure that surrounds us. The theory and the practice are interrelated in fascinating ways. Whether one thinks that the purpose of a college education is to prepare students for the workplace or to develop foundational knowledge with lifetime benefits (or both), computer science, in the 21st century, is fundamental.

So, we both agree that we want all students in higher education to take a course in computer science — but he doesn’t want that course to be a requirement. He explains:

When starting out at Princeton, I thought about lobbying for a computer science requirement and asked one of my senior colleagues in the physics department how we might encourage students to take a course. His response was this: “If you do a good course, they will come.” This wisdom applies in spades today. A well-designed computer science course can attract the vast majority of students at any college or university nowadays — in fact, there’s no need for a requirement.

Colleges and universities offer the opportunity for any student to take as many courses as they desire in math, history, English, psychology and almost any other discipline, taught by faculty members in that discipline. Students should have the same opportunity with computer science.

I have heard this argument before. Colleagues at Stanford have pointed out that most of Stanford undergraduates take their courses, without a requirement. Many colleagues have told me that a requirement would stifle motivation and would make students feel that we were forcing computer science down their throats. They would rather “attract” students (Sedgewick’s word above). I recognize that most students at the University of Michigan where I now work have the kind of freedom and opportunity that Sedgewick describes.

But I know many institutions and situations where Sedgewick’s description is simply not true. CS teachers can try attracting students, but they are only going to get them if it’s a required course.

• In most Engineering programs with which I am familiar, students have relatively few electives. In most of the years that I was at Georgia Tech, Nuclear Engineering students had no elective hours — if they took even a single course out of the planned, lockstep four years, the degree would take them longer (and cost them more). Mechanical Engineering students had exactly one three credit hour course that they could choose over a four year program. These are programs where students are not allowed to take any course that they desire. Those two degree programs are extreme situations (but still exist at many institutions). Even here in Computer Science & Engineering at the University of Michigan, students are limited in the number of elective hours that they can take outside of CS and engineering classes. For many technical degrees, if you want students to take a particular subject, you have to convince the faculty who own that degree to include computer science in those requirements. At Georgia Tech, we had to sell a computer science requirement to the rest of campus. I tell that story here.
• If students are paying by the course, then they take the courses that they need, not the ones that they might desire. I worked with Chattahoochee Technical College when I was in Georgia. They struggled to get students to even finish the requirements for a certificate. Students would take the small number of courses that helped them to gain the job skills that they needed. Completing courses that were merely interesting or recommended to them was simply an unnecessary expense. There are millions of students in US community colleges. If you really think that everyone should learn computer science, you have to think about reaching those millions of students, too.
• Finally, there are the students who might end up loving computer science but, right now, they don’t desire it. First, they may simply not know what computer science is. In California (as an example), only 39% of high schools offer computer science, and only 3% of all California high school students take it (see link here). Alternatively, they may know what computer science is, but have already decided that it’s not for them. In my group’s research, we use expectancy-value theory often to explain student behavior — if students don’t think that they’ll succeed in computer science, or don’t see people like them belonging in computer science, then they won’t take it. That’s one reason for mandatory computer science in high schools — to give everyone the chance to discover a love in CS. So far, it’s not working in US high schools. I have argued that (see article here) we don’t know how to ramp up to that at the high school level yet. But we certainly could at the higher education level.
  

I understand Sedgewick’s argument for why he wants to offer the most compelling computer science course he can at Princeton, in order to attract students and motivate them to learn about computing. (I don’t agree with him that curated online videos are better than live lectures, but I think we mean something different by “lectures.”) I also understand why making that course a requirement might undermine his efforts to motivate and engage students. But that’s only a small percentage of students at the Princeton-like institutions in the US, and elsewhere. I’m sure he would agree that everyone deserves the opportunity to learn about computer science. His reasons why CS is important for Princeton students are valid for everyone. We are going to need different strategies to reach everyone. For some students, a requirement is the only way that we are going to make it available to them.

Our work at SIGCSE 2019: Ebooks, jobs, privilege, what is research, and what is literacy

There was a time before each SIGCSE Symposium or ICER Conference that I would write a blog post about all the cool things being presented from Georgia Tech (see for example SIGCSE 2018 and SIGCSE 2017 posts).  But now, the list of things at SIGCSE 2019 from Georgia Tech and University of Michigan (my new home) is enormous.

I’m going to take a different strategy — the selfish and easier strategy.  Let me just tell you about the things that I know about because I work with the authors. All the proceedings are available here.

• Wednesday is the RESPECT conference (see program here). At the 9:30 session, Katie Cunningham will be one of the presenters of a paper “Job Placement Experience and Perceptions of Alumni from a Three-year Computer Science Program” with Miguel Lara and Bude Su about the job placement experience of students who graduate from the CSin3 program that won a best paper award at last year’s SIGCSE Symposium.
• On Thursday afternoon, I’m going to be on a panel with Lauren Margulieux, Leo Porter, Greg Nelson, and our organizer and moderator, Colleen Lewis.  The topic of the panel is “Negotiating Varied Research Goals in Computing Education Research.”  Greg and Amy Ko published a paper at this last year’s ICER, which won one of the best paper awards, that I and others strongly disagreed with.  Amy’s blog post captures the discussion and arguments well. This panel continues that discussion.
• Friday morning: I will be giving a keynote “Computing Education as a Foundation for 21st Century Literacy.”  I’ve practiced it every seminar and event I could find for the last couple months, so I’m ready.
• At Friday’s 10 am poster session, ECEP will be presenting a poster (Jeff Xavier as lead) on “Fostering State-level Change In CS Education: The Expanding Computing Education Pathways Alliance.”
• At a Friday morning 10:45 am special session, Miranda Parker will join Helen Hu, Jason Black, and Colleen Lewis to explore the role of privilege in CS education — an issue near and dear to Miranda’s heart (see, for example, this post).
• At Friday afternoon’s 3 pm poster session, Katie Cunningham will have a poster with Miguel Lara and Bude Su echoing their RESPECT paper.
• Friday afternoon at 3:45, Barb Ericson will be on a panel with Alison Derbenwick Miller, Lecia Barker, and Owen Astrachan on broadening participation in computing.  “You don’t have to be a white male that was learning how to program since he was five:” Computer Use and Interest From Childhood to a Computing Degree. I know that Barb is working hard on analyzing all the AP CS 2018 data, so this may be the unveiling of her annual analysis (see 2017 and 2016 posts here).
• Saturday afternoon, Barb Ericson (with Brad Miller and Jackie Cohen of U-M) will present a workshop on how to use the Runestone ebook platform that we use in our work (see, for example, this post).

I highly encourage everyone to check out the SIGCSE 2019 Best Paper award-winning presentations (see list here).  I’m particularly excited that Andreas Stefik’s (with Richard Ladner, William Allee, and Sean Mealin) project on developing CS Principles materials for blind and visually-impaired students is being recognized. We need to take more seriously making computing accessible to a broader range of students.

 

Do we want STEM education or do we want STEM learning?

I’ve mentioned a couple times that I’m working on using programming in teaching social sciences.  The goal is to teach STEM concepts (e.g., modeling, simulation, using graphical representations like charts, thinking about bias/skew and missing variables in big data, etc.), but in non-STEM subjects.  I argue that the “non-STEM subjects” part is key if you want diversity, if you want to draw in people who aren’t naturally going to show up in STEM classes.

I bounced this off an NSF program officer, and I got a pretty strong: “No.”  I’ll quote part of the response here.

While this is an intriguing idea, no, it would not be fundable in the XXX program as it does not involve the engagement of STEM faculty or their courses, assessments, or materials, or STEM majors.  (All of these are not necessary, but STEM is necessary, not just STEM learning.)

XXX is not just about improving or supporting STEM learning.  It is about improving STEM education.

There’s a distinction being drawn here between “STEM learning” and “STEM education.”  It’s an interesting and important distinction. I’m not at all saying that the officer is wrong.  This program officer is saying (paraphrasing), “It’s not just about learning STEM concepts. It’s about supporting the infrastructure and mechanisms through which we teach STEM.” (By the way, since this exchange, I’ve found other NSF officers in other programs that are more focused on STEM learning not just STEM education.)

That’s a fair concern. We do need STEM classes, curricula, assessments, and faculty. But if we really care about interdisciplinarity and broadening participation, we need to care about more than that.  We need to fund efforts to integrate STEM learning and use STEM thinking (e.g., Bacon’s Novum Organum) across the curriculum, to influence how we think about everything. We also need the infrastructure to support the institution of STEM education. The challenge is doing both.

There is an obvious connection to computing education.  We need more computer science teachers, curricula, tools, and classes. But we also need more students learning about computing, which might happen more inexpensively in mathematics, science, and social science classes. How do we prioritize?

What is programming-as-literacy, what does it look like, and what should we worry about? Alan Kay in Scientific American

Last month, I wrote a blog post about programming as a kind of literacy. I got some pushback.  Really? Literacy?  That programming in C stuff?  Well, no, programming in C is not what I mean by a form of literacy.  I recommended looking at some of what Alan Kay had written in Scientific American.

I decided to do that for myself.

Alan’s first article for Scientific American was in 1977, “Microelectronics and the Personal Computer,”  about the idea of a personal computer and the explorations they were doing at Xerox PARC with Smalltalk. I liked this one a lot because it emphasizes simulations “the central property of computing.”

The second was in 1984, “Computer Software.” Here’s where he defines literacy with the computer. It’s way more than just programming.

The third was in 1991, “Computers, Networks and Education.” This is the one where Alan really questioned whether things with computing were going in the right direction. For example, he worried about how people thought about “literacy” on the computer.

He returned to the importance of simulation.

And he was worried about people being critical of information that they find on the Internet (note that this is 1991, before Web browsers).

But in the end, Alan was hopeful, that we might develop a skeptical attitude with computing.

Computational thinking abstracts too far from the computer: We should teach CS with inquiry

Judy Robertson has a blog post that I really enjoyed: What Children Want to Know About Computers. She argues that computational thinking has abstracted too far away from what students really want to know about, the machine.

Computational thinking has been a hugely successful idea and is now taught at school in many countries across the world. Although I welcome the positioning of computer science as a respectable, influential intellectual discipline, in my view computational thinking has abstracted us too far away from the heart of computation – the machine. The world would be a tedious place if we had to do all our computational thinking ourselves; that’s why we invented computers in the first place. Yet, the new school curricula across the world have lost focus on hardware and how code executes on it.

Her post includes pictures drawn by children about what they think is going on inside of the computer.  They’re interested in these things!  We should teach them about it.  One of the strongest findings in modern science education is that inquiry works. Students learn science well if it’s based in the things that they want to know. Judy argues that kids want to know about the computer and how code executes on the computer. We shouldn’t be abstracting away from that. We should be teaching what the kids most want to learn.

To be clear, I am not criticizing the children, who were curious, interested and made perfectly reasonable inferences based on the facts they picked up in their everyday lives. But I think that computer science educators can do better here. Our discipline is built upon the remarkable fact that we can write instructions in a representation which makes sense to humans and then automatically translate them into an equivalent representation which can be followed by a machine dumbly switching electrical pulses on and off. Children are not going to be able to figure that out for themselves by dissecting old computers or by making the Scratch cat dance. We need to get better at explicitly explaining this in interesting ways.

MicroBlocks Joins Conservancy #CSEdWeek

This is great news for fans of GP and John Maloney’s many cool projects. MicroBlocks is a form of GP. This means that GP can be funded through contributions to the Conservancy.

We’re proud to announce that we’re bringing MicroBlocks into the Conservancy as our newest member project. MicroBlocks provides a quick way for new programmers to jump right in using “blocks” to make toys or tools. People have been proclaiming that IoT is the future for almost a decade, so we’re very pleased to be able to support a human-friendly project that makes it really easy to get started building embedded stuff. Curious? Check out a few of the neat things people have already built with MicroBlocks.

MicroBlocks is the next in a long line of open projects for beginners or “casual programmers” lead by John Maloney, one of the creators of Squeak (also a Conservancy project!) and a longtime Scratch contributor. MicroBlocks is a new programming language that runs right inside microcontroller boards such as the micro:bit, the NodeMCU and many Arduino boards. The versatility and interactivity of MicroBlocks helps users build their own custom tools for everything from wearables to model rockets or custom measuring devices and funky synthesizers.

Source: MicroBlocks Joins Conservancy

The systemic factors that limit Black participation in the Tech sector

I learned a lot from Kamau Bobb’s recent Atlantic article, “The Black Struggle for Technology Jobs.”  In it, he details the systemic factors that limit Black participation in the Tech sector.  He uses the possibility of Amazon’s HQ2 going to Atlanta as a framing.

After Atlanta made the shortlist of cities vying for Amazon’s second global headquarters, HQ2, it submitted a multibillion-dollar investment to try to seal the deal. (Other cities’ proposals were even bigger.) At stake is nothing less than the city’s economic future: HQ2 promises more than 50,000 high-tech jobs with an average salary of more than $100,000. With the tech industry looking like the future of all industry, Atlanta landing Amazon’s HQ2 would be a dream come true.

But a dream for whom? Highly educated people, particularly those with technical skills, are the ones who are really eligible for these prized jobs. People without that kind of education risk becoming even more marginalized in an increasingly tech-driven economy. In Atlanta, one of the most segregated cities in the United States, history has already largely determined who gets to benefit from the potential of Amazon.

In 2016, there was only one census tract in Atlanta where the population was more than 65 percent black, and where more than half the population age 25 or older had a bachelor’s degree or higher. In 2000, there were 10. Here, many black and brown students, and poor students of all backgrounds, receive a substandard education that does not prepare them for entry to the select colleges and universities tech companies draw their workforces from. Consequently, with or without Amazon’s investment, the city’s black population likely won’t land stem jobs unless they can gain access to the rigorous educational paths required to compete for them. In Atlanta and the many other American cities still scarred by decades of racist education policies, the future of work is still largely defined by a past from which their residents of color can’t seem to break free.

I’m biased in favor of this article because one of the students he interviews in this piece is my daughter, Katie. I learned from Katie’s comments, too.  I knew that the public high school where we sent all three of our children was unusually diverse, yet it was a family conversation how the gifted/accelerated classes were almost all white and Asian.  Because of what Barb and I do, we kept an eye on the AP CS class at that high school, and were surprised every year at how few Blacks ever entered the class, despite the significant percentage of Black students in the school. I’m glad that, years later, Katie still thinks about those issues and why so few Black students made it into her AP classes.

 

Literature is to Composition, as Computer Science is to Computational Literacy/Thinking

Annette Vee was visiting in Ann Arbor, and looked me up. We had coffee and a great conversation.  Annette is an English Professor who teaches Composition at University of Pittsburgh (see website here). She published a book last year with MIT Press, Coding Literacy: How Computer Programming is Changing Writing. (I’m part way through it and recommend it!) She knew me from this blog and my other writing about computational literacy. I was thrilled to meet someone who makes the argument for code-as-literacy with a real claim to understanding literacy.

One of the themes in our conversation was the distinction between literature and composition.  (I’m going to summarize something we were talking about — Annette is not responsible for me getting things wrong here.) Literature is about doing writing very well, about writing great works that stand the test of time. It’s about understanding and emulating greater writers.  Composition is about writing well for communication. It’s about letters to Grandma, and office memos, and making your emails effective.  Composition is about writing understandable prose, not great prose as in literature. People in literature sometimes look down on those in composition.

There’s a similar distinction to be made between computer science as it’s taught in Universities and what Annette and I are calling coding/computational literacy (but which might be what Aman Yadav and Shuchi Grover are calling computational thinking).  Computer science aims to prepare people to engineer complex, robust, and secure systems that work effectively for many users. Computational literacy is about people using code to communicate, to express thoughts, and to test ideas. This code doesn’t have to be pretty or robust. It certainly shouldn’t be complex, or nobody will do it. It should be secure, but that security should probably be built into the programming system rather than expecting to teach people about it (as Ben Herold recently talked about).  People in computer science will likely look down on those teaching computational literacy or computational thinking. That’s okay.

Few people will write literature. Everyone will compose.

When do we know that a programming course is not working for non-CS majors?

There’s a good discussion going on in Facebook that I wanted to make more public and raise as a question here.  The crush of undergraduates in CS today is making it difficult to offer tailored introductory CS courses to different majors.  The problem is particularly acute when designing instruction for future CS teachers.  If you put the CS teachers in the same course as the CS majors, it’s cheaper and easier — you just teach one big course, rather than multiple smaller courses. But is it as effective?

Some of my colleagues suggest that we can design undergraduate introductory computing courses that are effective for both non-CS and CS majors.  Here’s my question: How do you know when it’s not working?  At what point would you admit that the one course option isn’t working? It’s an interesting empirical question.

Here are some possible measures:

• Learning is a tricky measure.  For any discipline, the majors in that discipline are more motivated to learn more than the outside-the-discipline majors. You can’t expect the non-CS majors to learn more than the CS majors.  Then again, the non-CS majors probably come in knowing less.  If you do pre- and post-tests on CS knowledge, do non-CS majors have as large of a gain as the CS majors?  I don’t know, but in any case, it’s not a great measure for deciding if a class is succeeding for the non-CS majors.
• Taking more CS courses may be an effective measure, but only if you have more than one course that’s useful to non-CS majors.  If the rest of the classes are about software development, then non-CS majors will probably not want to go on, even if the intro course was effective and well-designed.
• Retention is a reasonable measure.  If more of the non-CS majors are dropping out from the course than the CS majors, you may not be meeting their needs.
• My favorite measure is relevance.  I argued in my blog post on Monday that programming is a practice that is relevant to many communities. Do the non-CS majors see the relevance of computing for them and their community after the introductory course?  If not, I don’t think it’s meeting their needs.
• Another tricky measure is use. Should non-CS majors be able (after their first course) to build some program that they find useful?  Certainly, if you achieve that goal, you have also achieved relevance.  How do you judge useful?  CS faculty may not be good judges of what a non-CS major would find useful, and CS faculty are most likely going to assess in terms of code quality (e.g., modularization, appropriate variable and function/module names, commenting, code style, etc.), which I consider pretty unimportant for as a measure for the non-CS students’ experience in the first course.

What do you think?  How would you know if your intro course was meeting non-CS students’ needs?

What would convince faculty in other disciplines that programming is useful?

Recently I came across an article from the journal Issues in Information Systems, “Faculty perspectives on the information technology and analytics requirements of business students.” The authors surveyed 204 business faculty from 20 different universities.  They found that “[N]early a third of respondents (32.6%) felt that computer programming skills should not be required at all. Interestingly, the same number (32.6%) also believe that Calculus should not be required of business students.”  Below is the table with the results.  About a third of faculty actually thought that all business students should take a three credit hour course in programming, but a third also felt that it shouldn’t be required at all. Details in the table below:

I’ve been working with the Georgia Department of Education on a new kind of pre-calculus course that uses computing to demonstrate the pre-calc concepts in a variety of contexts, e.g., scalar multiplication of a vector by reducing red in all the pixels in a picture, matrix multiplication by doing transforms of objects in 3-D space, periodicity of functions (like trigonometric functions) to generate sounds, etc. We did a careful mapping of each pre-calculus learning objective to relevant computing demonstrations, with multiple possible computing contexts for each pre-calculus learning objective. The course was rejected by the mathematics oversight board today. They didn’t buy it at all.  Among the responses: “The description of the course states that it is ‘designed to prepare students for calculus and other college level mathematics courses,’ which they believe it does not” and “Members feel that computer science is not mathematics and should not be replacing a mathematics course.”

I’m struck by these two stories.  For me, programming is this useful new notation that can enhance learning in many disciplines.  I’m swayed by the results with Bootstrap and with the CT-STEM effort at Northwestern. I hadn’t realized the extent to which the teachers in the non-CS disciplines were not buying the story.

• Business faculty are clearly dubious about the benefits of programming for business students.  I wonder if they’ve done the studies about how many business school graduates use programming (from SQL queries and spreadsheet macros, to data analysis and even modeling and simulation) in their daily jobs.
• Mathematics faculty are clearly dubious that (a) programming to apply mathematics topics leads to more mathematics learning and (b) computer science is even related to mathematics.

These create an interesting set of research questions to me. Why are faculty in non-CS disciplines dubious about the advantages of programming for their students?  What do they think programming is?  Maybe they’re right — maybe “programming” as we are currently defining it isn’t worth the credit hours for their students. How could we re-define programming (and programming languages and tools) to make it more useful?