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.

Maybe there’s more than one kind of Computational Thinking, but that makes research difficult

Shuchi Grover has a nice post in Blog@CACM where she suggests that there is more than one kind of Computational Thinking, which tries to resolve some of the concerns about the term (some of which I discussed here):

It’s also clear to me that in order to help make better sense of CT, we must acknowledge and distinguish two views of CT for K-12 education that are defined and operationalized based on the context for teaching/learning/application. One is a view of CT as a thinking skill for CS classrooms, that includes programming and other CS practices with the goal of highlighting authentic disciplinary practices and higher-order thinking skills used in computer science. The other is CT as a thinking skill/problem-solving approach in non-CS settings—this is often about using programming to automate abstractions of phenomena in other domains or work with data with the goal of better understanding phenomena (including making predictions and understanding potential consequences of actions), innovating with computational representations, designing solutions that leverage computational power/tools, and engaging in sense making around data.

She says that their are two “views” of CT, but she does distinguish Wing’s original definition which most people don’t buy. So, it seems like there are three.  (Kudos to Shuchi for pointing out that Seymour Papert actually uses the phrase “computational thinking” in Chapter 8 of Mindstorms — so cool!)

But I’m still wondering: Why do we have to call all of these things “computational thinking”?  I get that there’s a lot of energy around the term, but it’s an overloaded term.  Think about it from the perspective of any other science.  If you discovered that a species of animal or bacteria you were studying was actually two species, you’d name them differently.  In the 19th century, physicists thought that light traveled through a “luminiferous aether,” but now, nobody uses that term because we realized that such a thing didn’t exist. Maybe we as scientists should invent some new and more accurate terms instead of overloaded and confusing “computational thinking”?  If we’re using “computational thinking” because it has marketing cachet with teachers and principals (even if the term isn’t useful to researchers), that makes it hard to have a science around computing education.  Do we write about CT Type-1 vs CT Type-2?

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.