The website https://www.madewithcode.com/ is really nice, with high-quality videos. I like the direction. It’s not clear to me how all the different Google initiatives in CS education integrate. Does MadeWithCode, CS First, their new CS teaching repository, and the CS Fellows program all fit together in a strategic direction?
Made with Code’s mission is anchored by a website where girls can use basic coding technique to make bracelets and other items; Google also will dole out grants to host girl-coding parties at Girl Scouts and Boys and Girls Clubs around the country, as well as fund a range of marketing and other awareness campaigns.The idea is to de-couple coding with dry tech chores, and instead show how the skill is vital to everything from movie-making to helping cure malaria.
Roger Schank (one of the founders of both cognitive science and learning science) declares MOOCs dead (including Georgia Tech’s OMS degree, explicitly), while recommending a shift to Mentored Simulation Experiences. I find his description of MSE’s interesting — I think our ebook work is close to what he’s describing, since we focus on worked examples (as a kind of “mentoring”) and low cognitive-load practice (with lots of feedback).
So, while I am declaring online education dead, because every university is doing it and the market will soon be flooded with crap, I am not declaring the idea of a learning by doing mentored experience dead.
So, I propose a new name, Mentored Simulated Experiences.
The title is right, but the article (linked below) doesn’t really explain what “encouragement” means. We do have an answer to that from our “Georgia Computes!” work. We found that a sense of “belonging” was key to retention in the Computing major, especially for women and under-represented minorities.
More encouragement will be needed to attract girls into the IT profession, according to a BCS survey.
BCS, The Chartered Institute for IT, found that 79% of BCS members believed that the IT profession would benefit from having more women working in it.
Currently, women account for just 15-18% of IT professionals, a figure that has fallen significantly in recent years, said the BCS.
Interesting post on how STEM isn’t all male-dominant, but Engineering and CS are SO male dominant, it shifts the average.
Computer science is a particularly strange case, as it has seen more fluctuation both in raw numbers of students data not shown here and gender balance than any other field. Other fields have seen large shifts in gender balance, but they have generally been gradual and nearly monotonic—not reversing course in the early 1980s. It seems to me that the biggest drops in the ratio of women in CS came at times when the overall number of students in CS was dropping like after the dot-com bubble burst in the 2000. When CS grew, the number of women grew faster than the number of men. When CS shrunk, the number of women shrunk faster than the men. Perhaps if CS education had had a steady growth, rather than the boom-and-bust cycles that have plagued it since the late 1970s, it would not have had such a mysterious rise and fall in proportion of women in the field. The boom-and-bust cycles are not driven by the real need for CS degrees, but by media hype about relatively small shortages or excesses of personnel. I believe that the demand for CS degrees has been stabler than the supply unlike most other fields, where the supply has been steady even as demand has fluctuated.
The below-linked article by Jill Lepore is remarkable for its careful dissection of Christensen’s theory of “disruptive innovation.” (Thanks to Shriram Krishnamurthi for the link.) As Lepore points out, Christensen’s theories were referenced often by those promoting MOOCs. I know I was told many times (vehemently, ferociously) that my emphasis on learning, retention, diversity was old-fashioned, and that disrupting the university was important for its own sake, for the sake of innovation. As Lepore says in the quote below, there may be good arguments for MOOCs, but Christensen’s argument from a historical perspective just doesn’t work. (Ian Bogost shared this other critical analysis of Christensen’s theory.)
I just finished reading Michael Lewis’s The Big Short, and I see similarities between how Lepore describes reactions to Christensen’s theory of “disruptive innovation” and how Lewis describes the market around synthetic subprime mortgage bond-backed financial instruments. There’s a lot of groupthink going on (and the Wikipedia description is worth reading), with the party line saying, “This is all so great! This is a great way to get rich! We can’t imagine being wrong!” What Lewis points out (most often through the words of Dr. Michael Burry) is that markets work when there is a logic to them and real value underneath. Building financial instruments on top of loans that would never be repaid is ludicrous — it’s literally value-less. Lepore is saying something similar — innovation for its own sake is not necessarily valuable or a path to success, and companies that don’t disruptively innovate can still be valuable and successful.
I don’t know enough to critique either Lewis or Lepore, but I do see how the lesson of value over groupthink applies to higher-education. Moving education onto MOOCs just to be disruptive isn’t valuable. We can choose what value proposition for education we want to promote. If we’re choosing that we want to value reaching students who don’t normally get access higher education, that’s a reasonable goal — but if we’re not reaching that goal via MOOCs (as all the evidence suggests), then MOOCs offer no value. If we’re choosing that we want students to learn more, or to improve retention, or to get networking opportunities with fellow students (future leaders), or to provide remedial help to students without good preparation, those are all good value propositions, but MOOCs help with none of them.
Both Lewis and Lepore are telling us that Universities will only succeed if they are providing value. MOOCs can only disrupt them if they can provide that value better. No matter what the groupthink says, we should promote those models for higher-education that we can argue (logically and with evidence) support our value proposition.
In “The Innovative University,” written with Henry J. Eyring, who used to work at the Monitor Group, a consulting firm co-founded by Michael Porter, Christensen subjected Harvard, a college founded by seventeenth-century theocrats, to his case-study analysis. “Studying the university’s history,” Christensen and Eyring wrote, “will allow us to move beyond the forlorn language of crisis to hopeful and practical strategies for success.” … That doesn’t mean good arguments can’t be made for online education. But there’s nothing factually persuasive in this account of its historical urgency and even inevitability, which relies on a method well outside anything resembling plausible historical analysis.
At the NCWIT Summit this year, I heard an interesting concern. If CS counts as a mathematics or science course towards high school graduation requirements, will that make CS even less diverse? Should we keep CS as a business topic (elective) where the women and under-represented minorities are?
I took up that question for my Blog@CACM post for this month: Why Counting CS as Science or Math is Not Considered Harmful. I argue that our goal is universal computational literacy, with everyone using computing in every class and everyone taking CS. I don’t really care how it gets a foothold in schools. It was fun to write about Alan Kay, Adele Goldberg, and Andy diSessa, pointing out that they were talking about these ideas a long time before computational thinking.
The article posted below is a carefully-considered (not a “Rah! Rah! Let’s Code!”) and intriguing consideration of the role of coding in modern notion of literacy. I particularly liked the idea below. Is Annettee Vee right? Does knowing about coding inform your ability to think about things to code? I suspect that’s true, but it’s an empirical question. It’s much nearer transfer, and is not as much of a stretch as looking for evidence of general problem-solving skills from programming (which is very rare) or applying a computational framework for understanding the world (i.e., computational thinking).
The happy truth is, if you get the fundamentals about how computers think, and how humans can talk to them in a language the machines understand, you can imagine a project that a computer could do, and discuss it in a way that will make sense to an actual programmer. Because as programmers will tell you, the building part is often not the hardest part: It’s figuring out what to build. “Unless you can think about the ways computers can solve problems, you can’t even know how to ask the questions that need to be answered,” says Annette Vee, a University of Pittsburgh professor who studies the spread of computer science literacy.