Computer Science was always supposed to be taught to everyone, and it wasn’t about getting a job: A historical perspective

I gave four keynote talks in the last two months, at SIGITE, Models 2021 Educators’ Symposium, VL/HCC, and CSERC. I’m honored to be invited to them, but I do suspect that four keynotes in six weeks suggest some “personal issues” in planning and saying “No.” Some of these were recorded, but I don’t believe that any of them are publicly available

The keynotes had a similar structure and themes. (A lot easier than four completely different keynotes!) My activities in computing education these days are organized around two main projects:

• Defining computing education for undergraduates in the University of Michigan’s College of Literature, Science, and Arts (see earlier blog post referencing this effort);
• Participatory design of Teaspoon languages (mentioned most recently in this blog post).
  

My goal was to put both of these efforts in a historical context. My argument is that computer science was originally invented to be taught to everyone, but not for economic advantage. I see the LSA effort and our Teaspoon languages connected to the original goals for computer science. The talks were similar to my SIGCSE 2019 keynote (blog post about that talk here, and video version here), but puts some of the early history in a different perspective. I’m not going to go into the LSA Computing Education effort or Teaspoon languages here. I’m writing this up because I hope that it’s a perspective on the early history that might be useful to others.

I start out with C.P. Snow.

My PhD advisor, Elliot Soloway, would have all of his students read this book, “The Two Cultures.” Snow was a scientist who bemoaned the split between science and humanities in Western culture. Snow mostly blamed the humanities. That wasn’t Elliot’s point for having us read his book. Elliot wanted us to think about “Who could use what we have to teach, but might not even enter our classroom?”

This is George Forsythe. Donald Knuth claims that George Forthye first published the term “computer science” in a paper in the Journal of Engineering Education in 1961. Forsythe argued (in a 1968 article) that the most valuable parts of a scientific or technical education were facility with natural language, mathematics, and computer science.

In 1961, the MIT Sloan School held a symposium on “Computers and the World of the Future.” It was an amazing event. Attendees included Gene Amdahl, John McCarthy, Alan Newell, and Grace Hopper. Martin Greenberger’s book in 1962 included transcripts of all the lectures and all the discussants’ comments.

C.P. Snow’s chapter (with Norbert Wiener of Cybernetics as discussant) predicted a world where software would rule our lives, but the people who wrote the software would be outside the democratic process. He wrote, “A handful of people, having no relation to the will of society, having no communication with the rest of society, will be taking decisions in secret which are going to affect our lives in the deepest sense.” He argued that everyone needed to learn about computer science, in order to have democratic control of these processes.

In 1967, Turing laureate Peter Naur made a similar argument (quoting from Michael Caspersen’s paper): “Once informatics has become well established in general education, the mystery surrounding computers in many people’s perceptions will vanish. This must be regarded as perhaps the most important reason for promoting the understanding of informatics. This is a necessary condition for humankind’s supremacy over computers and for ensuring that their use do not become a matter for a small group of experts, but become a usual democratic matter, and thus through the democratic system will lie where it should, with all of us.” The Danish computing curriculum explicitly includes informing students about the risks of technology in society.

Alan Perlis (first ACM Turing Award laureate) made a different argument in his chapter. He suggested that everyone at University should learn to program because it changes how we understand everything else. He argued that you can’t think about integral calculus the same after you learn about computational iteration. He described efforts at Carnegie Tech to build economics models and learn through simulating them. He was foreshadowing modern computational science, and in particular, computational social science.

Perlis’s discussants include J.C.R. Licklider, grandfather of the Internet, and Peter Elias. Michael Mateas has written a fascinating analysis of their discussion (see paper here) which he uses to contextualize his work on teaching computation as an expressive medium.

In 1967, Perlis with Herb Simon and Alan Newell published a definition for computer science in the journal Science. They said that CS was “the study of computers and all the phenomena surrounding them.” I love that definition, but it’s too broad for many computer scientists. I think most people would accept that as a definition for “computing” as a field of study.

Then, we fast forward to 2016 when then-President Obama announced the goal of “CS for All.” He proposed:

Computer science (CS) is a “new basic” skill necessary for economic opportunity and social mobility.

I completely buy the necessity part and the basic skill part, and it’s true that CS can provide economic opportunity and social mobility. But that’s not what Perlis, Simon, Newell, Snow, and Forsythe were arguing for. They were proposing “CS for All” decades before Silicon Valley. There is value in learning computer science that is older and more broadly applicable than the economic benefits.

The first name that many think of when talking about teaching computing to everyone is Seymour Papert. Seymour believed, like Alan Perlis, “that children can learn to program and learning to program can affect the way that they learn everything else.”

The picture in the lower right of this slide is important. On the right is Gary Stager, who kindly shared this picture with me. On the left is Wally Feurzeig who implemented the programming language Logo with Danny Bobrow, when Seymour was a consultant to their group at BBN. In the center is Cynthia Solomon who collaborated with Seymour on the invention of the Turtle (originally a robot, seen at the top) and the development of Logo curriculum.

Cynthia was the lead author of a recent paper describing the history of Logo (see link here), which included the example of early Logo use on the upper right of this slide, which generates random sentences. Logo is named for the Greek word logos for “word.” The first examples of Logo were about manipulating natural language. Logo has always been used as an expressive medium (music, graphics, storytelling, and animation), as well as for learning mathematics (see the great book Turtle Geometry).

This is the context in which I think about the work with the LSA Computing Education Task Force. Our question was: At an R1 University with a Computer Science & Engineering undergraduate degree and an undergraduate BS in Information (with tracks in information analysis and user experience (UX) design), what else might undergraduates need? What are the purposes for computing that are broader and older than the economic advantages of professional software development? We ended up defining three themes of what LSA faculty do with computing and what they want their students to know:

• Computing for Discovery – LSA computational scientists create models and simulate them (not just analyze data that already exists), just as Alan Perlis suggested in 1961.
• Computing for Expression – Computing has created new ways for humans to express themselves, which is important to study and to use to explore, invent, and create new forms of expression, as the Logo community did starting in the 1960’s.
• Computing for Justice – LSA scholars investigate how computing systems can encode and exacerbate inequities, which requires some understand of computing, just as C.P. Snow talked about in 1961.

We develop our Teaspoon languages to meet the needs of teachers in teaching non-CS and even non-STEM classes. We argue that there are computing education learning objectives that we address with Teaspoon languages, even if they don’t include common languages features like for, while, and if statements. A common argument against our work in Teaspoon languages is that we’re undertaking a Sisyphean task. Computing is what it is, programming languages are what they are, and education is not going to be a driving force for changing anything in computing.

And yet, that’s exactly how the desktop user interface was invented.

Alan Kay (another Turing laureate in this story), Adele Goldberg, and Dan Ingalls led the development of Smalltalk in Xerox PARC in the 1970’s. The goal for Smalltalk was to realize Alan’s vision of a Dynabook, using the computer as a tool for learning. The WIMP (overlapping Windows, Icons, Menus, and mouse Pointer) interface was invented in order to achieve computing education goals. For the purposes of education, the user interface that you are using right now was invented.

The Smalltalk work tells us that we don’t have to accept computing as it is. Computing education today focuses mostly on preparing students to be professional software developers, using the tools of professional software development. That’s important and useful, but often eclipses other, broader goals for learning computing. The earliest goals for computing education are different from those in most of today’s computing education. We should question our goals, our tools, and our assumptions. Computing for everyone is likely going to look different than the computing we have today which has been defined for a narrow set of goals and for far fewer people than “all.”

From Guided Exploration to Possible Adoption: Patterns of Pre-Service Social Studies Teacher Engagement with Programming and Non-Programming Based Learning Technology Tools

In October, Bahare Naimipour presented our paper ”From Guided Exploration to Possible Adoption: Patterns of Pre-Service Social Studies Teacher Engagement with Programming and Non-Programming Based Learning Technology Tools” (Naimipour, Guzdial, Shreiner, and Spencer, 2021) at the Society for Information Technology and Teacher Education (SITE) 2021 conference. (Draft of the paper is available here. Full paywall version available here.) This paper is the first one about our work with social studies teachers since we received NSF funding. It was also a report on our last face-to-face participatory design session (in March 2020) before the pandemic lockdown. And most importantly, it was our first session with our data visualization tool DV4L in the mix.

I have blogged about our participatory design sessions before (see Bahare’s FIE paper from last Fall). Basically, we set up a group of social studies teachers in pairs, then ask them to try out various visualization tools with activity sheets that we have created to scaffold their process. The goal is to get everyone to make a visualization successfully in less than 10 minutes, and leave time to explore or try one (or both) other tools. There is time for the pairs to persuade each other to (a) come try the cool tool they found or (b) avoid this tool because it’s too hard or not useful. The tools in this set were Vega-Lite (a declarative programming tool which our teachers have found complex but useful in the past), CODAP (a drag-and-drop visualization tool designed for middle and high school students), and our DV4L (a purpose-built visualization tool that makes code visible but not required).

The teachers saw value in having students build visualization themselves (e.g., “I think making your own data visualization allows for a deeper connection and understanding of the data.”) As we hoped, they teased out what they liked and disliked about the tools. Most of the teachers preferred DV4L over the other two tools, because of its simplicity. Critically, they felt that they were engaging with the inquiry and not the tool: “(With DV4L) I found myself asking questions connected to the data itself, rather than asking questions in order to figure out how to work the visual.”

That teachers found DV4L easier than Vega-Lite isn’t really surprising. We were pleased that teachers weren’t disappointed with DV4L’s more limited visualization capabilities. What was really surprising was that our teachers preferred DV4L to CODAP, and this has happened in successive in-service teacher participatory design sessions during the pandemic. CODAP is drag-and-drop, creates high-quality visualizations, and was designed explicitly for middle and high school students. A teacher in one of our in-service design sessions explained to me why she preferred DV4L to CODAP. “CODAP is really powerful, but it would take me at least three hours to get my students comfortable with it. Is it worth it?” Just how much visualization is any social studies teacher going to use? Again, too much focus on the tool gets in the way of the social studies inquiry.

Now you might be asking, “But Mark, do the students learn history with DV4L? And do they see and learn about computing?” Great questions — we’re not there yet. Here’s one of our big questions, after running several more participatory design sessions with teachers since the lockdown: Why aren’t teachers adopting DV4L in their classrooms? They tell us that they really like it. But nobody’s adopted yet. How do we go from “ooh, great tool!” to “and here’s my lesson plan, and we’ll use it next week”? That’s an active area of research for all of us right now.

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.

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.

Please forward to high school history teachers: Task-specific programming in social studies data viz

Hi,

We built this tool, DV4L (Data Visualization for Literacy), to help teachers quickly create data visualizations for social study classes. This is currently a minimum viable product (MVP) version of our tool, and we would like to collect your feedback so that we can improve! 

• Please take a few moments and try out our tool at http://b48ca06e.ngrok.io/index.html
• We intend for DV4L to be intuitive and easy to use, so you shouldn’t need any instructions to start. (If you ever see a screen with ‘TOO MANY CONNECTIONS’ on the top, please just wait for a few seconds and refresh the page.)
• After you have tried out the tool, please take a few minutes to fill out this survey at https://forms.gle/jASed4f7GfTZD9xa8. 
  

We would really appreciate it if you could do this by Sunday Dec 8th to meet our class requirements. 

Feel free to share this with any other teachers or students who would like to try this out. Thank you so much!

I have been working with a team of four University of Michigan Computer Science students (in a class with Elliot Soloway) to develop a data visualization tool for history classes. They have a prototype ready for testing, and they need user data for their class by Sunday, December 8. Could you please forward this to any high school (or middle school) history teachers you know? They would also love to get some feedback from high school history students, too.

Here are three reasons why I’m excited about this tool.

First, the team really listened to us, our history professor collaborator (Tammy Shreiner), and the social studies teachers who gave us feedback on different data visualization tools. One of the students drove 2.5 hours to attend a participatory design session with social studies teachers in October. As an example, there are driving questions for each database, to guide students in what they might inquire about — that’s a specific request from Tammy.

Second, this is a tool for history inquiry. Data visualization tools like Vega-Lite and CODAP are terrific. (Readers of this blog know how impressed I am by Vega-Lite.) But they’re not designed for inquiry. As Bob Bain has taught me, historical inquiry starts from two pieces of evidence or two accounts that disagree. This tool is designed to support comparing different pieces of evidence, and maintaining a trace of what you’ve explored. Inquiry is about comparison, not from building a single visualization.

Third, this is task-specific programming in a subtle and interesting way. You build visualizations by making choices from pull-down menus on the left. As you find interesting graphs, you save them to the right. When you want to remember what the graph is about, you hover over it and get a textual representation of what pull-down menus generated this graph. I’m arguing that hover text is a program —- it’s a representation of the process for generating that graph, and it serves as a reminder of where you’ve been. It’s a program whose value is in reading it, not executing it.

Amy Ko told me once (I’m paraphrasing) that a program is a description of a process for the future. Using a tool is for now. A program represents the future. This program could be executed by the user in the future — set the pop-up menus to the same values, and you’ll get the same visualization. More importantly, the program represents the past and serves as a reminder for what you can do next.

Please do pass this around so that our team can get a sense of what’s working and what’s not in this prototype. Thanks!

Computer Science Teachers as Provocateurs: All learning starts from a problem

One of the surprising benefits of working with social science educators (history and economics) has been new perspectives on my own teaching. I’ve studied education for several years, and have worked with science and mathematics education researchers in the past. It hadn’t occurred to me that history education is so different that it would give me a new way of looking at my own teaching.

Last week, I was in a research meeting with Bob Bain, a history and education professor here at U. Michigan. He was describing how historians understand knowledge and what historian’s practice looks like, and how that should be reflected in the history classroom.

He said that all learning in history starts from a problem. That gave me pause. What’s a “problem” in history?

Bob explained that he defines problem as John Dewey did, as something that disturbs the equilibrium. “Activities at the Dewey School arose from the child’s own interests and from the need to solve problems that aroused the child’s curiosity and that led to creative solutions.” We don’t think until our environment is disturbed, but that environment may just be in your own head.

We each have our own stories that we use to explain the world, and these make up our own personal equilibria. Maybe students have always been told that the American Civil War was about states’ rights, and then they read the Georgia Declaration of Secession. Maybe they’ve thought of Columbus as the explorer who discovered America, and then note that he wasn’t celebrated until 1792, 300 years after his arrival. Why wasn’t he celebrated earlier, and why him and at that time? A good history teacher sets up these conflicts, disequilibria, or problems. Bob says it can be easy enough to create, simply by showing two contrasting accounts of the same historical event.

Research in the learning sciences supports this definition of learning. Roger Schank talked about the importance of learning through “expectation failure.” You learn when you realize that you don’t know something:

The understanding cycle – expectation failure – explanation – reminding – generalization – is a natural one. No one teaches it to us. We are not taught to have goals, nor to attempt to develop plans to achieve those goals by adapting old plans from similar situations. We need not be taught this because the process is so basic to what comprises intelligence. Learning is a natural act.

In progressive education, we’re told that the teacher should be a “Guide on the Side, not the Sage on the Stage.” When Janet Kolodner was developing Learning By Design, she talked about the role of teacher as coach and orchestrator. Those were roles I was familiar with. Bob was describing a different role.

I challenged him explicitly, “You’re a provocateur. You create the problems in the students’ minds.” He agreed.

Bob got me thinking about the role of the teacher in the computer science class. We can sometimes be a guide, a coach, and orchestrator — when students are working away on some problem or project. But sometimes, we have to be the provocateur.

We should always start from a problem. In science education, this is easy. Kids naturally do wonder why the sky is blue, why sunsets are more red, why heat travels along metal but not wood, and why stars twinkle. In more advanced computer science, we can also start from questions that students’ already have. I’m taking a MOOC right now because it explains things I’ve wondered about.

But in introductory classes, students already use a computer without problems. They may not see enough of real computing to wonder about how it works. The teacher has to generate a problem, inculcate curiosity — be a provocateur.

We should only teach something when it solves a problem for the student. A lecture on variables and types should be motivated by a problem that the variables and types solve. A lecture on loops should happen when students need to do something so often that copy-pasting the code repeatedly won’t work. Saying “You’re going to need this later” is not motivation enough — that doesn’t match the cycle that Schank described as natural. Nobody remembers things they will need in the future. Learning results when you need new knowledge to resolve the current problem, disequilibria, or conflict.

Note: Computer science doesn’t teach problem-solving. Dewey’s and Schank’s point is that problem-solving is a natural way in which people learn. Learning to program still doesn’t teach problem-solving skills.

Higher Ed and the Role of a Computing Culture: Interview on No Such Thing Podcast

When I visited the To Code and Beyond workshop last month (as mentioned in this podcast), I was interviewed by Marc Lesser for the No Such Thing podcast, which he just released.  (My keynote is also in his podcast series here.) It’s a wide ranging interview, from woodworking to the work I’ve started with History professors Bob Bain and Tamara Shreiner, from how I began teaching computing in 1980 to how I’ve been inspired by (to name a few) Alan Kay, Yasmin Kafai, E. Paul Goldenberg, Brian Harvey, and Bob Kozma.  Marc’s framing for the podcast is interesting (pasted below): How do we “bottle” me? In other words: How do we create more computing educators who care about CS for All, especially at the higher education level? The episode can be found here.

At the top of the last episode you learned about Mark Guzdial. Mark is a Professor in the College of Engineering at the University of Michigan. After his talk at Cornell Tech’s “To Code and Beyond” I had a chance to sit down with Mark and ask what questions had bubbled up while I listened to his talk live. Probably my most pressing question: what you’re saying is great, but we’ve all seen professors like you on youtube – Mark is a brilliant, animated, ukulele playing Computer Science professor, who, from my time with him, seems as passionate about you learning about his passion topic, as he is about the topic itself. He’s a rare mix, and what I’m sure many in the audience wondered – what the country is wondering right now – is how do we bottle some of that, and help thousands of teachers in every state offer young people the experience that surely the students in Mark’s class have each semester. For what it’s worth, out-of-state tuition at his school is $43,476 with a 26% acceptance rate. A wicked problem, indeed.

The Negative Consequences of Brown v Board of Education: Integrating Computing Education

The second season of Revisionist History has just finished.  This season didn’t have the same multiple episodes with tight ties to the issues of education as last season (as I described in this blog post), there was one standout episode that does relate to our issues: Miss Buchanan’s Period of Adjustment.  The podcast deals with the negative consequences of the Brown v Board of Education Supreme Court case that declared that separate was not equal and forced schools to integrate.  The well-documented consequence of the integration was the closing of the schools for African-Americans and the firing of Black school teachers.  Gladwell first considers what the Brown family (named in the case) and the other families in the case actually wanted, and about the longterm impact that even today, there are disproportionately few African-American teachers in the US are African-American — and that leads to impacts on students.

When I studied Brown v Board of Education when I was a graduate student at the University of Michigan, we were taught a negative consequence that Gladwell barely touches on.  Gladwell mentions that there were few jobs for an educated Black person at the time of Brown v Board.  The Supreme Court’s decision, and the consequent firing of Black teachers, was an enormous blow to the African-American middle class in the United States.  Employment was lost at a large scale, and longterm impacts on wealth and prosperity can be measured today.

The connection to computer science education is part of the question of how do we reach everyone and help everyone to succeed.  Today’s computing education is de facto segregated — not in the sense of colored vs white classes, but in terms of only certain demographics are in CS classes and other demographics are not.

• In many of the high schools we work with, even if white and Asian students are in the school population minority, the computer science classes are mostly white and Asian.
• English CS classes are almost entirely male, maybe even more than in the US (described here).
• US undergraduate CS classes don’t seem to be retaining women (blog post here).
• Code.org classes have are almost half poor students (blog post here), and have excellent diversity (see their Medium post here). What are the rich students taking?  The diversity that Code.org is seeing is not reflected in undergraduate CS (see Generation CS report) which has little diversity and has mostly prosperous students. That’s important because undergraduate CS is the path that most students will take to the IT industry, which is mostly white/Asian and male.

How do we improve diversity in computing education?  Can we avoid a heavy-handed and expensive mandate like requiring CS for everyone? I side effect of requiring everyone to take CS might be that we get all the same kind of CS.  Can we provide equal access to everyone without the negative consequences that Gladwell describes from Brown v Board of Education?

Brown v Board of Education might be the most well-known Supreme Court decision, a major victory in the fight for civil rights. But in Topeka, the city where the case began, the ruling has left a bittersweet legacy. RH hears from the Browns, the family behind the story.

Source: Revisionist History Podcast

The Invented History of ‘The Factory Model of Education’: Personalized Instruction and Teaching Machines aren’t new

When I was a PhD student taking Education classes, my favorite two-semester sequence was on the history of education.  I realized that there wasn’t much new under the sun when it comes to thinking about education.  Ideas that are key to progressive education movements date back to Plato’s Republic: “No forced study abides in a soul…Therefore, you best of men, don’t use force in training the children in the studies, but rather play. In that way you can also better discern what each is naturally directed toward.”  Here we have learning through games (but not video games in 300BC) and personalized instruction — promoted over 2400 years ago.  I named my dissertation software system Emile after Rousseau’s book with the same name whose influence reached Montessori, Piaget, and Papert decades later.

Audrey Watters takes current education reformers to task in the article linked below.  Today’s reformers don’t realize the history of the education system, that many of the idea that they are promoting have been tried before. Our current education system was designed in part because those ideas have already failed.  In particular, the idea of building “teaching machines” as a response to “handicraft” education was suggested over 80 years ago.  Education problems are far harder to solve than today’s education entrepreneurs realize.

Many education reformers today denounce the “factory model of education” with an appeal to new machinery and new practices that will supposedly modernize the system. That argument is now and has been for a century the rationale for education technology. As Sidney Pressey, one of the inventors of the earliest “teaching machines” wrote in 1932 predicting “The Coming Industrial Revolution in Education,”

Education is the one major activity in this country which is still in a crude handicraft stage. But the economic depression may here work beneficially, in that it may force the consideration of efficiency and the need for laborsaving devices in education. Education is a large-scale industry; it should use quantity production methods. This does not mean, in any unfortunate sense, the mechanization of education. It does mean freeing the teacher from the drudgeries of her work so that she may do more real teaching, giving the pupil more adequate guidance in his learning. There may well be an “industrial revolution” in education. The ultimate results should be highly beneficial. Perhaps only by such means can universal education be made effective.

via The Invented History of ‘The Factory Model of Education’.

The reality is that technology never has and never will dramatically change education (as described in this great piece in The Chronicle).  It will always be a high-touch endeavor because of how humans learn.

Education is fundamentally a human activity and is defined by human attention, motivation, effort, and relationships.  We need teachers because we are motivated to make our greatest efforts for human beings with whom we have relationships and who hold our attention.

In the words of Richard Thaler, there are no Econs (see recommended piece in NYTimes).

The Day the Purpose of College Changed: What was the impact on CS Education?

The article linked below makes the argument that then-Governor Ronald Reagan changed perception higher education in the United States when he said on February 28, 1967 that the purpose of higher education was jobs, not “intellectual curiosity.”  The author presents evidence that date marks a turning point in how Americans thought about higher education.

Most of CS education came after that date, and the focus in CS Education has always been jobs and meeting industry needs.  Could CS Education been different if it had started before that date?  Might we have had a CS education that was more like a liberal education?  This is an issue for me since I teach mostly liberal arts students, and I believe that computing education is important for giving people powerful new tools for expression and thought.  I wonder if the focus on tech jobs is why it’s been hard to establish computing requirements in universities (as I argued in this Blog@CACM post). If the purpose of computing education in post-Reagan higher education is about jobs, not about enhancing people’s lives, and most higher-education students aren’t going to become programmers, then it doesn’t make sense to teach everyone programming.

The Chronicle of Higher Education ran a similar piece on research (see post here).  Research today is about “grand challenges,” not about Reagan’s “intellectual curiosity.”  It’s structured, and it’s focused.  The Chronicle piece argues that some of these structured and focused efforts at the Gates Foundation were more successful at basic research than they were at achieving the project goals.

“If a university is not a place where intellectual curiosity is to be encouraged, and subsidized,” the editors wrote, “then it is nothing.”

The Times was giving voice to the ideal of liberal education, in which college is a vehicle for intellectual development, for cultivating a flexible mind, and, no matter the focus of study, for fostering a broad set of knowledge and skills whose value is not always immediately apparent.

Reagan was staking out a competing vision. Learning for learning’s sake might be nice, but the rest of us shouldn’t have to pay for it. A higher education should prepare students for jobs.

via The Day the Purpose of College Changed – Faculty – The Chronicle of Higher Education.

First PhD in CS in US went to a Sister

An interesting excursion into the history of computing.  One of the first two PhD’s in Computer Science in the United States went to a female and a member of a religious order!  I would never have guessed.

But at virtually the same time in June 1965, two other degrees were completed: Sister Mary Kenneth Keller, BVM, earned a Ph.D. from the Computer Sciences Department at the University of Wisconsin, and Irving C. Tang earned a D.Sc. from the Applied Mathematics and Computer Science Department at Washington University in St. Louis. The purpose of this article is to show that in the United States, Keller and Tang were not just earlier but also first, thereby providing a more accurate historical record.

via Who Earned First Computer Science Ph.D.? | blog@CACM | Communications of the ACM.

Top Secret Rosies: Rediscovering WWII’s female ‘computers’

Thanks to Fred Martin for forwarding this link.  What a great story!  Have to get the DVD.

Jean Jennings Bartik was one of the women computers. In 1945, she was a recent graduate of Northwest Missouri State Teachers College, the school’s one math major. She lived on her parents’ farm, refusing the teaching jobs her father suggested, avoiding talk of marrying a farmer and having babies. Bartik was waiting on a job with the military…

She learned the hand calculations, and saw the clunky old analyzer used to speed up the process. Its accuracy depended on the work of her colleagues, and a mechanic who serviced its belts and gears.

The war ended in 1945, but within a couple months of arriving in Philadelphia, Bartik was hired to work on a related project — an electronic computer that could do calculations faster than any man or woman. The Electronic Numerical Integrator and Computer, created by Penn scientists John Mauchly and J. Presper Eckert Jr., weighed more than 30 tons and contained about 18,000 vacuum tubes. It recognized numbers, added, subtracted, multiplied, divided and a few other basic functions.

Men had built the machine, but Bartik and her colleagues debugged every vacuum tube and learned how to make it work, she said. Early on, they demonstrated to the military brass how the computer worked, with the programmers setting the process into motion and showing how it produced an answer. They handed out its punch cards as souvenirs. They’d taught the massive machine do math that would’ve taken hours by hand.

But none of the women programmers was invited to the celebratory dinner that followed. Later, the heard they were thought of as models, placed there to show off the machine.

via Rediscovering WWII’s female ‘computers’ – CNN.com.

Oral History of CS Videos

Posted to SIGCSE-members —  a great finish to CSed Week.

CS Ed Week – take a look at the newly launched YouTube CEOHP channel

( http://www.youtube.com/user/CEOHP )

containing short video interviews with Computing Educators. This is part
of the Computing Educators Oral History Project whose newly revamped
website launched today ( http://ceohp.org ) .

This NSF-funded project will be archived and hosted by the Charles
Babbage Institute. It will continue to be under development with
curricular materials and additional interviews being added in the near
future.

Barbara Boucher Owens and Vicki Almstrum