We should be emphasizing design of computing over teaching computational thinking

Alan Kay, Cathie Norris, Elliot Soloway, and I have an article in this month’s Communications of the ACM called “Computational Thinking Should Just Be Good Thinking.” (See link here, and a really nice summary at U-M which links to a preprint draft.) Our argument is that “computational thinking” is already here — students use computing every day, and that computing is undoubtedly influencing their thinking. But that fact is almost trivial. What we really care about is effective, critical, “expanded” thinking where computing can play a role in helping us think better. To do that, we need better computing.

It’s more important to improve computing than to teach students to think with existing computing. The state of our current tools is poor. JavaScript wasn’t designed to be learnable and to help users think. (Actually, I might have just stopped with “JavaScript wasn’t designed.”) We really need to up our game, and we should not be focusing solely on how to teach students about current practices around iteration or abstraction. We should also be about developing better designs so that we spend less time on the artifacts of our current poor designs.

Ken Kahn called us out, in the comments at the CACM site, suggesting that general-purpose programming tools are better than building specialized programming tools. I wrote a Blog@CACM post in response “The Size of Computing Education, By-The-Numbers.” We have so little success building tools that reach large numbers of students that it doesn’t make sense to just build on our best practice. They may all be local maxima. We should try a wide variety of approaches.

I got asked an interesting question on Twitter in response to the article.

Do you think @Bootstrapworld and @BerkeleyDataSci Data 8 modules both embody your philosophy?

I don’t think we’re espousing a philosophy. We’re suggesting a value for design and specifically improved design of computing.

Bootstrap clearly does this. The whole Bootstrap team has worked hard to build, iterate, test, and invent. If you haven’t seen it, I recommend Shriram Krishnamurthi’s August 2019 keynote at the FCRC. They solved some significant computer science design problems in creating Bootstrap.

Berkeley’s Data 8 is curriculum about existing tools, R and Jupyter notebooks. That’s following an approach like most of computational thinking — the focus is on teaching the existing tools. That’s not a bad thing to do, but you end up spending a lot of time teaching around the design flaws in the existing tools. I just don’t buy that R or Jupyter notebooks are well-designed for students. We can do much better. LivelyR (see link here) is an example of trying to do better.

We should be teaching students about computing. But computing is also the most flexible medium humans have ever invented. We should be having an even greater emphasis on fixing, designing, and inventing better computing.


Many thanks to Barbara Ericson, Amy Ko, Shriram Krishnamurthi, and Ben Shapiro who gave me comments on versions (multiple!) of this essay while it was in development. They are not responsible for anything we said, but it would be far less clear without them. The feedback from experts was immensely valuable in tuning the essay. Thanks!

November 13, 2019 at 2:00 am 3 comments

Come to the CUE.NEXT Workshop: Making computing education work for all undergraduates

I’m going to be the keynoter at the Dec. 5 workshop in DC. The workshop series is near and dear to my heart — how do we make computing education accessible to all undergraduates? Below is taken from the CRA website here.

CUE

CS Departments have seen significant enrollment increases in undergraduate computer science courses and programs. The number of non-majors in CS courses has also increased significantly, and many CS departments cannot meet the demand. One key reason for the increased demand from non-majors is the fact that computing and computer science have become relevant to undergraduate education in all disciplines. However, there is currently no consensus on how to design computing courses or how to structure curricula aimed at teaching the fundamentals of CS and computing to students who need to use computing effectively in the context of the other disciplines.

The goal of the upcoming CUE.NEXT workshops — organized by Larry Birnbaum (Northwestern), Susanne Hambrusch (Purdue), and Clayton Lewis (UC Boulder) — is to initiate a national dialog on the role of computing in undergraduate education. Computing educators and CS departments, as well as colleagues and academic units representing other stakeholder disciplines, will work together to define and address the challenges. Three NSF funded workshops are scheduled to take place in Chicago (November 18 and 19), DC (December 5 and 6) and Denver (January 2020).

November 11, 2019 at 7:00 am Leave a comment

Freakonomics misunderstands what public education is, how it works, and how to change it

I am a fan of Freakonomics Radio. I have heard all the old ones (some more than once), and I keep up with the new ones. Freakonomics informs and inspires me, including many posts in this blog. So, I want to respond when they get it really wrong.

Episode 391 America’s Math Curriculum Doesn’t Add Up (see link here) is hosted by Steven Levitt (the economist) rather than the usual host Stephen Dubner (the journalist). The podcast is inspired by the struggles Levitt’s teenage children face with their mathematics classes. Levitt contends that the US mathematics curriculum is out-dated and in serious need of reform. I agree with his premise. His interviews with Jo Boaler and Sally Sadoff are interesting and worth listening to. But there are huge holes in his argument, and his solution makes no sense at all.

Part of his argument is based on a poll they took through the Freakonomics twitter account.

MARTSCHENKO: So, we’ve been putting together a survey that we sent out to Freakonomics listeners. We asked our survey respondents which subjects they use in their daily life, traditional math and data-related. So trigonometry, geometry, calculus, versus more data-related skills like analyzing and interpreting data and visualizing it.

LEVITT: So what percent of people, say, use calculus on a daily basis?

MARTSCHENKO: About 2 percent said that they use calculus on a daily basis, and almost 80 percent say they never use it.

LEVITT: Okay. I would think calculus would get used more than trigonometry and geometry, although that would be hard if only 2 percent are using it. But what percent use trigonometry and geometry?

MARTSCHENKO: Yeah. Less than 2 percent of respondents said that they use trigonometry in their daily life, but over 70 percent of them said that they never use it.

LEVITT: And how about geometry?

MARTSCHENKO: Geometry was a little bit better. There were about 4 percent of respondents who said that they use geometry daily, but again, over 50 percent said that they never use it.

LEVITT: So it’s a pretty sad day when we’re celebrating the use of geometry because 4 percent of the people report using it.

I don’t dispute his results. Even engineers don’t use geometry or trigonometry every day, but they have to learn it. We don’t only teach subjects that people use on a daily basis. I don’t think about the American Revolution or the three branches of the US government every day, but it’s important for American citizens to know how their country came to be and how it’s structured. We hope that every voter knows the roles that they’re voting for, though they may not think about them daily.

One of the reasons we teach what we do is to provide the tools to learn other important things. Engineers and scientists have to know geometry and trigonometry to do what they do. We could wait until undergrad to teach geometry, trig, and calc — but that’s pretty late. There’s an argument that we should tell students what science and engineering is really about (and show them the real math), both to encourage them and to fully inform them.

The Freakonomics on-line survey misunderstands why we teach what we teach. It’s not just about everyday. It’s also about the things that every student will need someday (like understanding how impeachment works) and about the things that might inspire them to think about a future day when they are people who use calculus and trigonometry.

The moment that made me exclaim out loud while listening to the podcast was in the interview with David Coleman, CEO of the College Board. Levitt wants to replace some (all?) of the high school mathematics curriculum with a focus on data science. That’s an interesting proposal worth exploring. Levitt makes an important point — how do we teach teachers about data science?

Levitt: But will teachers in AP Biology or AP Government have the skills to teach the data-fluency parts of their courses?

COLEMAN: One magnificent thing about teaching is, it’s often the most lively when the teacher himself or herself is learning something. I think the model of practiced expertise being the only way that teaching is exciting is false.

I think what’s more interesting is, can we create environments for teachers and students where together the data comes alive and fascinates them. The question is not to try to suddenly retrain the American teaching force to be data analysts, but instead design superb data experiences, superb courses, where the hunt for data and the experimentation is so lively that it excites them as well as their students. And then they together might be surprised at the outcomes.

I know of no data that says that a teacher’s “surprise” leads to better learning outcomes than a teacher who has significant content knowledge. Much the opposite — the evidence I know suggests that teachers only learn pedagogical content knowledge (how to teach the subject matter) when they develop sufficient expertise in the content area. Learning outcomes are improved by teachers knowing the content and how to teach it. The idea that classes are somehow better (more “lively”) when the teacher doesn’t know what’s going to happen makes no sense to me at all.

Finally, Levitt’s solution to reforming the mathematics curriculum is for all of us to sign a petition, because (he argues) there are only six to ten people in each state that we have to convince in order to reform each state’s mathematics curriculum.

LEVITT: So tell me, who makes the decisions? How does curriculum get set in the U.S., in education systems?

MARTSCHENKO: In public education, the people with power are those on the state boards of education. So each state will have a state board of education. There are typically six to 10 people on the board, and they’re the ones who make those decisions about the curriculum, what gets taught, how testing is done.

LEVITT: So literally this set of six to 10 people have the power to set the guidelines, say, for whether or not data courses are required.

MARTSCHENKO: That’s correct.

LEVITT: So what you’re implying is that each state sets its own standards. Okay, so there are these state boards of education who have all the power, it seems to me what you’re saying is, if we can get in front of those boards, and we can convince, say, even one of them of the wisdom of what we’re doing, they can flip a switch, although that’s probably way too simple, and put into motion a whole series of events which will lead in that state to the teaching of data being part of the math curriculum.

They have a petition (see link here) that they encourage people to fill out and send to their state boards.

He’s right that his solution is “way too simple.” In fact, for every state that I have worked with (16 states and Puerto Rico, as part of the ECEP Alliance), his description is downright wrong.

US States are all different, and they each own their own K-12 system. One of the important dimensions on which states differ is how much control remains at the state level (“state control”) and how much control is pushed down to districts and schools (“local control,” which is how California, Nebraska, and Massachusetts are all structured). What is being described is “state control,” but it still misses the complexity — it isn’t just the board that makes decisions. I have watched how Georgia (state control) and Michigan (local control) have created standards and curricula.

  • In Georgia, yes, there is a central control structure that makes decisions, but so many other people are involved to make anything happen. I was part of a Georgia Department of Education effort to create a precalculus course that included programming — this is coming from that centralized control. Our committee alone was six people. The course was stopped by another committee of math teachers (secondary and higher ed) who decided that “a course that included programming couldn’t also be math”. Let’s set aside whether they were right (I don’t think they were), the reality is that those math teachers should get a voice, even in a central control state. Even if those 6-10 people want something, you can’t just jam a new course down the throats of teachers who don’t want to teach it.
  • In Michigan, each individual school district makes its own decisions. (In California, high school graduation requirements can vary by district.) Yes, there are standards at the state level in Michigan, and those standards are supported by assessment tests that are state-wide, but the assessment tests don’t cover everything — districts have a lot of leeway. Even just setting standards goes way beyond the board. I’ve watched Michigan build both its social science and computer science standards while I’ve been here. The effort to build these standards are broad and involve teachers from all over the state. There are big committees, and then there are still lots of other people involved to make these standards work in the individual districts.

Let’s imagine that Levitt’s worldview was right — six to ten people make all the decisions. Play it out. Who sets the standards (desired learning standards) for the new data science focus? Not just those six to ten people. Who defines the curriculum — resources, lesson plans, and assessments? Who prepares the teachers to teach the new curriculum? And in a local control state, how do you enforce these new standards with all those districts? Nothing as big as changing the US math curriculum happens with just those six to ten people.

This last point is close to home for all of us in computing education. Every CS ed researcher I know who is in a CS department struggles with getting their colleagues to understand, appreciate, and use research-based methods. Even if the Chair is supportive, there are lots of teachers involved, each with their own opinion. How much more complicated is a whole state.

Education in the United States is a vast system. I’ve mentioned before that I have an Alan Kay quote on a post-it on my monitor: “You can fix a clock. You have to negotiate with a system.” You can’t fix math in the US education system. You can only negotiate with it.

Freakonomics misunderstands why the US education system exists the way that it does, what makes it work (informed teachers), and how decisions are made and executed within that system.

November 4, 2019 at 7:00 am 6 comments

Come to the NAS Workshop on the Role of Authentic STEM Learning Experiences in Developing Interest and Competencies for Technology and Computing

Register here. And view the agenda here.

November 4, 2019

1:00 p.m.–6:00 p.m. (reception hour following)

Workshop
Role of Authentic STEM Learning Experiences in Developing Interest and Competencies for Technology and Computing

Keck Building, Room 100
500 5th St., NW
Washington, DC

#STEMforCompTech

The Board on Science Education of the National Academies of Sciences, Engineering, and Medicine will host a public workshop on November 4, 2019 to explore issues in STEM education. The workshop will illustrate the various ways in which stakeholders define and conceptualize authentic STEM learning opportunities for young people in grades K-12 in formal and informal settings, and what that means for the goals, design, and implementation of such experiences. Presenters will unpack the state of the evidence on the role of authentic STEM learning opportunities and promising approaches and strategies in the development of interest and competencies for technology and computing fields. A recurring theme throughout the workshop will be implications for increasing diversity and access to authentic STEM learning experiences among underserved young people.
 
Confirmed Speakers:

  • Lisa Brahms, Monshire Museum of Science (virtual)
  • Loretta Cheeks, Strong TIES
  • Tamara Clegg, University of Maryland
  • Jill Denner, ETR
  • Ron Eglash, University of Michigan
  • Sonia Koshy, Kapor Center
  • Keliann LaConte, Space Science Institute (virtual)
  • Amon Millner, Olin College
  • Kylie Peppler, University of California, Irvine
  • Jean Ryoo, University of California, Los Angeles
  • Emmanuel Schanzer, Bootstrap
  • Shirin Vossoughi, Northwestern University (virtual)
  • David Weintrop, University of Maryland

Questions? Email us at STEMforCompTech@nas.edu

October 28, 2019 at 7:00 am 2 comments

How to change undergraduate computing to engage and retain more women

My Blog@CACM post for this month talks about the Weston et al paper (from last week), and about a new report from the Reboot Representation coalition (see their site here). The report covers what the Tech industry is doing to close the gender gap in computing and “what works” (measured both empirically and from interviews with people running programs addressing gender issues).

I liked the emphasis in the report on redesigning the experience of college students (especially female) who are majoring in computing.  Some of their emphases:

  • Work with community colleges, too.  Community colleges tend to be better with more diverse students, and it’s where about half of undergraduates start today.  If you want to attract more diverse students, that’s where to start.
  • They encourage companies to offer “significant cash awards” to colleges that are successful with diverse students. That’s a great idea — computer science departments are struggling to manage undergraduate enrollment these days, and incentives to keep an eye on diversity will likely have a big impact.
  • Grow computer science teachers and professors. I appreciated that second emphasis.  There’s a lot of push to grow K-12 CS teachers, and I think it’s working.  But there’s not a similar push to grow higher education CS teachers. That’s going to be a chokepoint for growing more CS graduates.

The report is interesting — I recommend it.

October 21, 2019 at 7:00 am Leave a comment

Results from Longitudinal Study of Female Persistence in CS: AP CS matters, After-school programs and Internships do not

NCWIT has been tracking their Aspirations in Computing award applicants for several years. The Aspirations award is given to female students to recognize their success in computing. Tim Weston, Wendy DuBow, and Alexis Kaminsky have just published a paper in ACM TOCE (see link here) about their six year study with some 500 participants — and what they found led to persistence into CS in College.  The results are fascinating and somewhat surprising — read all the way to the end of the abstract copied here:

While demand for computer science and information technology skills grows, the proportion of women entering computer science (CS) fields has declined. One critical juncture is the transition from high school to college. In our study, we examined factors predicting college persistence in computer science and technology related majors from data collected from female high school students. We fielded a survey that asked about students’ interest and confidence in computing as well as their intentions to learn programming, game design, or invent new technology. The survey also asked about perceived social support from friends and family for pursuing computing as well as experiences with computing, including the CS Advanced Placement (AP) exam, out-of-school time activities such as clubs, and internships. Multinomial regression was used to predict persistence in computing and tech majors in college. Programming during high school, taking the CS Advanced Placement exam, and participation in the Aspirations awards program were the best predictors of persistence three years after the high school survey in both CS and other technology-related majors. Participation in tech-related work, internships, or after-school programs was negatively associated with persistence, and involvement with computing sub-domains of game design and inventing new applications were not associated with persistence. Our results suggest that efforts to broaden participation in computing should emphasize education in computer programming.

There’s also an article at Forbes on the study which includes recommendations on what works for helping female students to persist in computing, informed by the study (see link here). I blogged on this article for CACM here.

That AP CS is linked to persistence is something we’ve seen before, in earlier studies without the size or length of this study.  It’s nice to get that revisited here.  I’ve not seen before that high school work experience, internships, and after-school programs did not work.  The paper makes a particular emphasis on programming:

While we see some evidence for students’ involvement in computing diverging and stratifying after high school, it seems that involvement in general tech-related fields other than programming in high school does not transfer to entering and persisting in computer science in college for the girls in our sample. Understanding the centrality of programming is important to the field’s push to broaden participation in computing.  (Italics in original.)

This is an important study for informing what we do in high school CS. Programming is front-and-center if we want girls to persist in computing.  There are holes in the study.  I keep thinking of factors that I wish that they’d explored, but they didn’t — nothing about whether the students did programming activities that were personally or socially meaningful, nothing about role models, and nothing about mentoring or tutoring.  This paper makes a contribution in that we now know more than we did, but there’s still lots to figure out.

 

 

 

October 14, 2019 at 7:00 am 8 comments

An Analysis of Supports and Barriers to Offering Computer Science in Georgia Public High Schools: Miranda Parker’s Defense

Miranda Parker defends her dissertation this Thursday.  It’s a really fascinating story, trying to answer the question: Why does a high school in Georgia decide (or not) to offer computer science?  She did a big regression analysis, and then four detailed case studies.  Readers of this blog will know Miranda from her guest blog post on the Google-Gallup polls, her SCS1 replication of the multi-lingual and validated measure of CS1 knowledge, her study of teacher-student differences in using ebooks, and her work exploring the role of spatial reasoning to relate SES and CS performance (work that was part of her dissertation study). I’m looking forward to flying down to Atlanta and being there to cheer her on to the finish.

Title: An Analysis of Supports and Barriers to Offering Computer Science in Georgia Public High Schools

Miranda Parker
Human-Centered Computing Ph.D. Candidate
School of Interactive Computing
College of Computing
Georgia Institute of Technology

Date: Thursday, October 10, 2019

Time: 10AM to 12PM EST

Location: 85 5th Street NE, Technology Square Research Building (TSRB), 2nd floor, Room 223

Committee:

Dr. Mark Guzdial (Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Betsy DiSalvo, School of Interactive Computing, Georgia Institute of Technology
Dr. Rebecca E. Grinter, School of Interactive Computing, Georgia Institute of Technology
Dr. Willie Pearson, Jr., School of History and Sociology, Georgia Institute of Technology
Dr. Leigh Ann DeLyser, CSforAll Consortium

Abstract:

There is a growing international movement to provide every child access to high-quality computing education. Despite the widespread effort, most children in the US do not take any computing classes in primary or secondary schools. There are many factors that principals and districts must consider when determining whether to offer CS courses. The process through which school officials make these decisions, and the supports and barriers they face in the process, is not well understood. Once we understand these supports and barriers, we can better design and implement policy to provide CS for all.

In my thesis, I study public high schools in the state of Georgia and the supports and barriers that affect offerings of CS courses. I quantitatively model school- and county-level factors and the impact these factors have on CS enrollment and offerings. The best regression models include prior CS enrollment or offerings, implying that CS is likely sustainable once a class is offered. However, large unexplained variances persist in the regression models.

To help explain this variance, I selected four high schools and interviewed principals, counselors, and teachers about what helps, or hurts, their decisions to offer a CS course. I build case studies around each school to explore the structural and people-oriented themes the participants discussed. Difficulty in hiring and retaining qualified teachers in CS was one major theme. I frame the case studies using diffusion of innovations providing additional insights into what attributes support a school deciding to offer a CS course.

The qualitative themes gathered from the case studies and the quantitative factors used in the regression models inform a theory of supports and barriers to CS course offerings in high schools in Georgia. This understanding can influence future educational policy decisions around CS education and provide a foundation for future work on schools and CS access.

October 7, 2019 at 7:00 am Leave a comment

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