BBC is giving away 1 million mini computers so kids can learn to code: Prediction — little impact on broadening participation
I agree that these boards are cool, but I’m a geeky white guy. I predict that they’ll have little impact in increasing access to computing education or in diversifying computing. Bare board computers are not more attractive to teachers, so we don’t get more teachers going into CS. They’re not more attractive than existing computers to women who aren’t already interested in computing. Why are people so excited about handing out bare board computers to grade school children? Is this just white males emphasizing the attributes that attract them? Judith Bishop of MSR (whose TouchDevelop will work on these new computers) says that she’s seen girls get engaged by these new computers, but nobody has done any research to see if that’s more than the 20% of females who get interested in computing now, or if that happens outside of the pilot classrooms.
Currently in development, the Micro Bit is a small piece of programmable, wearable hardware that helps kids learn basic coding and programming. It could act as a springboard for more advanced coding on products, such as the single-board computer Raspberry Pi, according to the BBC.
Children will be able to plug the device into a computer, and start creating with it immediately.
“BBC Make it Digital could help digital creativity become as familiar and fundamental as writing, and I’m truly excited by what Britain, and future great Britons, can achieve,” BBC director general Tony Hall said in a statement Thursday.
Nice blog post from Barbara Ericson exploring the lack of women in the new surge in CS undergraduate enrollment.
A Surge in Majors, but Where Are the Women?
While a number of colleges and universities in the United States have recently seen a tremendous increase in the number of students who want to major in computing, the percentage of women who are interested is still low. A study conducted by the Association for Computing Machinery and the WGBH Educational Foundation in 2008 found that only 9 percent of college-bound teen girls thought that a career in computing was a very good choice for them, and only 17 percent thought that it was a good career choice. Teen girls associated computing with typing, math, and boredom. While the percentage of bachelor’s degrees awarded to women in the United States did increase from 11.7 percent in 2010–11 to 12.9 percent in 2011–12, women are still dramatically underrepresented.
The Percentage of Women Taking the Computer Science AP Exam Lags
The Advanced Placement (AP) computer science A course is equivalent to a college-level introductory computer science course. It focuses on object-oriented programming in Java. In 2014, only about 20 percent of AP computer science A exam takers were women. While that was an increase from the previous year, when the percentage was 18.5 percent, it is still far below the percentage of women who took the AP calculus AB exam (48.7 percent) and the percentage of women who took the AP biology exam (59 percent). It is even well below the percentage of women who took the physics B exam (34.7 percent), as shown below.
My first thought when seeing this article was, “Well, I’m glad it’s not just CS.” (See my post about how recruiting teachers is our biggest challenge in CS10K.) And my second thought was, “WHERE are we going to get all the teachers we need, across subjects?!?” And how are we going to retain them?
Several big states have seen alarming drops in enrollment at teacher training programs. The numbers are grim among some of the nation’s largest producers of new teachers: In California, enrollment is down 53 percent over the past five years. It’s down sharply in New York and Texas as well.
In North Carolina, enrollment is down nearly 20 percent in three years.
“The erosion is steady. That’s a steady downward line on a graph. And there’s no sign that it’s being turned around,” says Bill McDiarmid, the dean of the University of North Carolina School of Education.
Why have the numbers fallen so far, so fast?
McDiarmid points to the strengthening U.S. economy and the erosion of teaching’s image as a stable career. There’s a growing sense, he says, that K-12 teachers simply have less control over their professional lives in an increasingly bitter, politicized environment.
Dr. Gary May, Dean of the College of Engineering at Georgia Tech, is one of my role models. I’ve learned from him on how to broaden participation in computing, what academic leadership looks like, and how to make sure that education gets its due attention, even at a research-intensive university.
He wrote an essay (linked below) critical of the idea of “STEAM” (Science, Technology, the Arts, and Mathematics). I just recently wrote a blog post saying that STEAM was a good idea (see link here). I’m not convinced that I’m at odds with Gary’s point. I suspect that the single acronym, “STEM” or “STEAM,” has too many assumptions built into it. We probably agree on “STEM,” but may have different interpretations of “STEAM.”
The term “STEM” has come to represent an emphasis on science, technology, engineering, and mathematics education in schools. A recent Washington Post article critiques exactly that focus: Why America’s obsession with STEM education is dangerous.
From Gary’s essay, I think he reads “STEAM” to mean “We need to integrate Arts into STEM education.” Or maybe, “We need to emphasize Arts as well as STEM in our schools.” Or even, “All STEM majors must also study Art.” Gary argues that STEM is too important to risk diffusing by adding Art into the mix.
That’s not exactly what I mean when I see a value for STEAM. I agree that STEM is the goal. I see STEAM as a pathway.
Media Computation is a form of blending STEM plus Art. I’m teaching computer science by using the manipulation of media at different levels of abstraction (pixels and pictures, samples and sounds, characters and HTML, frames and video) as an inviting entryway into STEM. There are many possible and equally valid pathways into Computing, as one form of STEM. I am saying that my STEAM approach may bring people to STEM who might not otherwise consider it. I do have a lot of evidence that MediaComp has engaged and retained students who didn’t used to succeed in CS, and that part of that success has been because students see MediaComp as a “creative” form of computing (see my ICER 2013 paper).
I have heard arguments for STEAM as enhancing STEM. For example, design studio approaches can enhance engineering education (as in Chris Hundhausen’s work — see link here). In that sense of STEAM, Art offers ways of investigating and inventing that may enhance engineering design and problem-solving. That’s about using STEAM to enhance STEM, not to dilute or create new course requirements. Jessica Hodgins gave an inspiring opening keynote lecture at SIGCSE 2015 (mentioned here) where she talked about classes that combined art and engineering students in teams. Students learned from each other new perspectives that informed and improved their practice.
“STEM” and “STEAM” as acronyms don’t have enough content to say whether we’ve in favor or against them. There is a connotation for “STEM” about a goal: More kids need to know STEM subjects, and we should emphasize STEM subjects in school. For me, STEM is an important goal (meaning an emphasis on science, technology, engineering, and mathematics in schools), and STEAM is one pathway (meaning using art to engage STEM learning, or using art as a valuable perspective for STEM learners) to that goal.
No one — least of all me — is suggesting that STEM majors should not study the arts. The arts are a source of enlightenment and inspiration, and exposure to the arts broadens one’s perspective. Such a broad perspective is crucial to the creativity and critical thinking that is required for effective engineering design and innovation. The humanities fuel inquisitiveness and expansive thinking, providing the scientific mind with larger context and the potential to communicate better.
The clear value of the arts would seem to make adding A to STEM a no-brainer. But when taken too far, this leads to the generic idea of a well-rounded education, which dilutes the essential need and focus for STEM.
At conferences like SIGCSE 2015 and at meetings like the CS Principles Advisory Board meeting in Chicago in February, I’m hearing from pilot teachers of the new AP CS Principles Curriculum (see website here) who are building Media Computation (specifically, in Python) into their classes. In the preface to the new 4th Edition (see Amazon page here), I went through the Big Ideas and Learning Objectives (as they were on the website at that time) that are being addressed in the new version. Explicitly, I added content to address CS Principles learning objectives, e.g., measuring two different algorithms by using clock time and manipulating “live” CSV data downloaded from websites.
Below is quoted from the preface:
The Advanced Placement exam in CS Principles has now been defined. We have explicitly written the fourth edition with CS Principles in mind. For example, we show how to measure the speed of a program empirically in order to contrast two algorithms (Learning Objective 4.2.4), and we explore multiple ways of analyzing CSV data from the Internet (Learning Objectives 3.1.1, 3.2.1, and 3.2.2).
Overall, we address the CS Principles learning objectives explicitly in this book as shown below:
- In Big Idea I: Creativity:
- LO 1.1.1: . . . use computing tools and techniques to create artifacts.
- LO 1.2.1: . . . use computing tools and techniques for creative expression.
- LO 1.2.2: . . . create a computational artifact using computing tools and techniques to solve a problem.
- LO 1.2.3: . . . create a new computational artifact by combining or modifyingexisting artifacts.
- LO 1.2.5: . . . analyze the correctness, usability, functionality, and suitability ofcomputational artifacts.
- LO 1.3.1: . . . use programming as a creative tool.
- In Big Idea II: Abstraction:
- LO 2.1.1: . . . describe the variety of abstractions used to represent data.
- LO 2.1.2: . . . explain how binary sequences are used to represent digital data.
- LO 2.2.2: . . . use multiple levels of abstraction in computation.
- LO 2.2.3: . . . identify multiple levels of abstractions being used when writingprograms.
- In Big Idea III: Data and information:
- LO 3.1.1: . . . use computers to process information, find patterns, and test hy-potheses about digitally processed information to gain insight and knowledge.
- LO 3.2.1: . . . extract information from data to discover and explain connections,patterns, or trends.
- LO 3.2.2: . . . use large data sets to explore and discover information and knowledge.
- LO 3.3.1: . . . analyze how data representation, storage, security, and transmission of data involve computational manipulation of information.
- In Big Idea IV: Algorithms:
- LO 4.1.1: . . . develop an algorithm designed to be implemented to run on a computer.
- LO 4.1.2: . . . express an algorithm in a language.
- LO 4.2.1: . . . explain the difference between algorithms that run in a reasonable time and those that do not run in a reasonable time.
- LO 4.2.2: . . . explain the difference between solvable and unsolvable problems in computer science.
- LO 4.2.4: . . . evaluate algorithms analytically and empirically for efficiency, correctness, and clarity.
- In Big Idea V: Programming:
- LO 5.1.1: . . . develop a program for creative expression, to satisfy personal curiosity or to create new knowledge.
- LO 5.1.2: . . . develop a correct program to solve problems
- LO 5.2.1: . . . explain how programs implement algorithms.
- LO 5.3.1: . . . use abstraction to manage complexity in programs.
- LO 5.5.1: . . . employ appropriate mathematical and logical concepts in programming.
- In Big Idea VI: The Internet:
- LO 6.1.1: . . . explain the abstractions in the Internet and how the Internet functions.
Repeatability presumes evidence (which can be repeated). Computer scientists have not valued evidence and repeatability as much as we need to for rigor and scientific advancement — in education, too. One of my favorite papers by Michael Caspersen is his Mental models and programming aptitude ITICSE 2007 paper where he and his colleagues attempt to replicate the results of the famous and controversial Dehnadi and Bornat paper (see here). Michael and his colleagues are unable to replicate the result, and they propose a research method for understanding the differences. That’s good science — attempting to replicate another’s result, and then developing the next steps to understand the differences.
Science advances faster when we can build on existing results, and when new ideas can easily be measured against the state of the art. This is exceedingly difficult in an environment that does not reward the production of reusable software artifacts. Our goal is to get to the point where any published idea that has been evaluated, measured, or benchmarked is accompanied by the artifact that embodies it. Just as formal results are increasingly expected to come with mechanized proofs, empirical results should come with code.
If a paper makes, or implies, claims that require software, those claims must be backed up.
Nice story and presentation from Katie Cunningham about how she informed her faculty about why there are so few women in CS, and what they can do about it.
I based the main arc of my presentation on a book chapter by Whitecraft and Williams that Greg Wilson of Software Carpentry was kind enough to forward to me. It’s an evenhanded look at much of the research in this area, including theories that are often out of favor in most places I frequent. It served as a great overview, though I felt it could have focused more on issues involving differences in prior programming experience pre-college and intimidation brought on by “nerdy strutting“. (Update: I just discovered a fantastic 2012 report by NCWIT that can also serve as a great overview. It covers cultural issues more comprehensively, with more recent research and more focus on the pre-college years.)