Posts tagged ‘computing education’
In my research, I’m most interested in the non-CS majors, the ones who learn computing because it makes them more productive (see where I make that argument) or because they want to make themselves more marketable (see Eric Robert’s post) or because they will live and work (as I predict) in the fat line between programmers and users (see post here). A recent article in the CACM suggests that all non-CS majors need to be learn (let’s not use the “be exposed” euphemism — there’s no sense in “exposing” someone to something unless you’d like them to learn from it) “functional programming languages [and] the declarative programming paradigm.” I’m willing to consider that, but why? The quote below says, “they allow programmers to do more with less and enable compilation to more efficient code across a wide range of runtime targets.” I’ve been studying non-CS majors who program for a lot of years, and I’ve never heard any of them say even once that they want to “enable compilation to more efficient code across a wide range of runtime targets.”
So let’s consider the “more with less.” Do we buy that what what non-CS majors is to be able to get more expressive power with fewer keystrokes? I don’t see the argument for that.
- Brian Dorn studied graphic designers who program, and found that assignment was fairly hard for them to learn (see his CHI 2010 paper). Surely, there’s not much that has fewer characters than that.
- Neil Brown has been mining the BlueJ Blackbox data for empirical data on what students get wrong most often (see his ICER paper). I was surprised to learn that confusing & for && and | for || is pretty common. Those are pretty easy to type, short, and seemingly error-prone expressions.
- We have Thomas Green’s fascinating result that that IF P THEN … END P; IF NOT P THEN … END NOT P. is not just better than IF P THEN…ELSE.… It’s ten times better — novices do better by a magnitude if they avoid ELSE.
My suspicion is that non-CS major programmers value understandability and fewer errors, over fewer keystrokes and more power.
I like functional programming and would be interested in a good argument for it for non-CS majors. I don’t see it here.
Second, would-be programmers (CS majors or non-majors) should be exposed as early as possible to functional programming languages to gain experience in the declarative programming paradigm. The value of functional/declarative language abstractions is clear: they allow programmers to do more with less and enable compilation to more efficient code across a wide range of runtime targets. We have seen such abstractions gain prominence in DSLs, as well as in imperative languages such as C#, Java, and Scala, not to mention modern functional languages such as F# and Haskell.
I was honored to serve on Michael Lee’s dissertation committee. Mike’s basic thesis is available at this link, or you can get the jumbo-expanded edition with an enormous appendix describing everything in his software plus his learning evaluation (described below) at this link. His thesis brings together several studies he’s done on Gidget, his game in which he teaches programming. I’ve written about his work before, like his terrific finding that including assessments improves engagement in his game (see blog post here) and about how Gidget offers us a new way to think about assessing learning (see blog post here).
Michael had several fascinating results with Gidget. One of my favorites that I have not blogged on yet was that personifying the programming tool improves retention (see his ICER 2011 paper here). When Gidget sees a syntax error, she (I’m assigning gender here) doesn’t say, “Missing semicolon” or “Malformed expression.” Instead, she says “I don’t what this is, so I’ll just go on to the next step” and looks sad that she was unable to do what the programmer asked her to do. The personification of the programming tool dramatically improved the number of game levels completed. They kept going. In course terms, they were retained.
The dissertation has yet another Big Wow result. Mike developed an assessment of computing knowledge based on Allison Elliott Tew’s work on FCS1 (see here). He did a nice job validating it using Amazon’s Mechanical Turk.
He then compares three different conditions for learning differences:
- Gidget, as a game for learning.
- CodeAcademy, as a tutorial for learning.
- The Gidget game level designer. The idea was to provide a constructionist learning environment without a curriculum. Mike wanted it be like using Scratch or Alice or any other open-ended creative programming environment. What would the students learn without guidance in Gidget?
Gidget and CodeAcademy are statistically equivalent for learning, and both blow away the constructionist option. A designed curriculum beats a discovery-based learning opportunity. That’s interesting but not too surprising. Here’s the wild part: The Gidget users spend 1/2 as much time. Same learning, half as much time. I would not have predicted this, that Mike’s game is actually more efficient for learning about CS than is a tutorial. I’ve argued that learning efficiency is super important especially for high school teachers (see post here).
Mike is now an assistant professor at the New Jersey Institute of Technology (see his web page here). I wish him luck and look forward to what he does next!
The world is about more than computing. It’s easy for those of us who live and work in CS to see it as CS-centric. I work in a section of Atlanta that is bursting with high-tech startups. I found this article compelling — not because it threw cold water on the vision of Atlanta as a “Silicon Valley East,” but because it painted a picture of how much more diverse the economy in Atlanta really is.
In reality, metro Atlanta’s relationship with the tech sector is, well, complicated.
Georgia boasts about 280,000 tech jobs, according to Technology Association of Georgia president and chief executive officer Tino Mantella — the great majority of them in metro Atlanta. But information technology jobs only make up about 3.5 percent of the area’s labor market, down from a peak of 4.7 percent in the 1990s, federal Bureau of Labor data shows.
And California, home to the real Silicon Valley, dominates venture capital investing — the lifeblood of tech startups — with 56 percent of spending compared to the 1 percent in Georgia, Mantella said.
I believe the result described in the article below, that a critical limitation of teacher’s ability to use technology is too little understanding of technology. In a sense, this is another example of the productivity costs of a lack of ubiquitous computing literacy (see my call for a study of the productivity costs). We spend a lot on technology in schools. If teachers learned more about computing, they could use it more effectively.
In 2010, for example, researchers Peggy A. Ertmer of Purdue University, in West Lafayette, Ind., and Anne T. Ottenbreit-Leftwich of Indiana University, in Bloomington, took a comprehensive look at how teachers’ knowledge, confidence, and belief systems interact with school culture to shape the ways in which teachers integrate technology into their classrooms.
One big issue: Many teachers lack an understanding of how educational technology works.
But the greater challenge, the researchers wrote, is in expanding teachers’ knowledge of new instructional practices that will allow them to select and use the right technology, in the right way, with the right students, for the right purpose.
I predict that if we did this study with CS teachers, we’d find the same result. The belief that CS is for males and not for females is deeply ingrained in the perceptions of our field. Kahneman would tell us that it’s part of our System 1 thinking (see NYTimes Book Review). What do you think teachers would draw if asked to “draw a computer scientist“? I predict that the gender bias that favors males as computer scientists would be greater for post-secondary teachers than for secondary or elementary teachers. Most secondary school CS teachers that I’ve met are sensitive to issues of gender diversity in computing, and they actively encourage their female students. Most post-secondary CS teachers with whom I’ve worked are not sensitized to issues of women in computing and have not changed how they teach to improve gender diversity (see example here).
In the study, teachers graded the math tests of 11-year-olds and, on average, the scores were lower for girls. But, when different teachers graded the same tests anonymously, the girls performed far better (out-performing the boys in many cases.)
Dr. Edith Sand, one of the researchers, told American Friends of Tel Aviv University, that the issue wasn’t overt and obvious sexism, but “unconscious discouragement.”
The study goes on to say that the gender biases held by elementary school teachers have an “asymmetric effect” on their students — the boys’ performance benefits and girls’ performance suffers based on the teacher’s biases. Boys do well because teachers believe they will, girls don’t because teachers believe they won’t.
Bobby Schnabel has just been named the new CEO of ACM. This is a big win for computing education. Bobby has been an innovator and leader in efforts to improve computing education policy and broaden participation in computing. Now, he’s in charge of ACM overall, the world’s largest computing professional organization. That gives him a big pulpit for promoting the importance of computing education.
Schnabel has a long history of service to the computing community. He has served in several capacities, including chair, of ACM’s Special Interest Group on Numerical Mathematics (ACM SIGNUM). When Schnabel assumes his role as CEO, he will step down as founding chair of the ACM Education Policy Committee, which led to the creation of Computer Science Education Week in the US, and the formation of the industry/non-profit coalition, Computing in the Core. Schnabel also serves as board member of code.org, and as a member of the advisory committee of the Computing and Information Science and Engineering directorate of the National Science Foundation. He has served as a board member of the Computing Research Association.
Dedicated to improving diversity in computing, Schnabel is a co-founder and executive team member of the National Center for Women & Information Technology (NCWIT), a major non-profit organization in the US for the full participation of girls and women in computing and information technology. He also serves as chair of the Computing Alliance for Hispanic-Serving Institutions Advisory Board.
I found the article below fascinating, but as an instance of a general model. The article describes how scientists who study gun control have very different opinions about gun control than the general American public — who (presumably) don’t draw on scientific evidence to inform their opinions. People who draw on evidence have different opinions than those who don’t. Most people do not draw on evidence when informing their opinions.
I don’t see that the story here is “Scientists are smart and the public is dumb.”
I would bet that if you asked these same gun control scientists about something outside of their area of expertise, they similarly ignore evidence. I work with CS professors all the time who draw on evidence to inform their opinions within their area of expertise (e.g., robotics, HCI, networking), but when it comes to education, evidence goes out the window. Davide Fossati and I did a study (yeah, evidence — we know what that’s worth) describing how CS faculty make decisions (see post here). In my experience, if the evidence is counter to their opinion, evidence is frequently ignored. One of the things we learned in “Georgia Computes!” was just how hard it is to change faculty (see our journal article where we tell this story). CS teachers are pretty convinced that they teach just fine, despite evidence to the contrary. I regularly try to convince my colleagues to teach using active learning approaches like peer instruction given the overwhelming evidence of its effectiveness (see this article, for just one), and I regularly get told, “It really doesn’t work for me.”
People are people, even when scientists and CS faculty.
Of the 150 scientists who responded, most were confident that a gun in the home increases the chance that a woman living there will be murdered (72 percent agreed, 11 percent disagreed), that strict gun control laws reduce homicide (71 percent versus 12 percent), that more permissive gun laws have not reduced crime rates (62 percent versus 9 percent), that guns are used more often in crimes that in self-defense (73 percent versus 8 percent), and that a gun in the home makes it a more dangerous place to be (64 percent versus 5 percent).
Eighty-four percent of the respondents said that having a firearm at home increased the risk of suicide.
These figures stand sharply at odds with the opinions of the American public. A November 2014 Gallup poll found that 63 percent of Americans say that having a gun in the house makes it a safer place to be, a figure that has nearly doubled since 2000. According to the same survey, about 40 percent of Americans keep a gun in the home.