Posts tagged ‘computing education’

African-Americans don’t want to play baseball, like women don’t want to code: Both claims are false

I listened to few of my podcasts this summer with our move, so I’m catching up on them now. I just heard one that gave me a whole new insight into Stuart Reges’s essay Why Women Don’t Code.

In Here’s Why You’re Not an Elite Athlete (see transcript here), they consider why:

In 1981, there was 18.7 percent black, African-American players in the major leagues. As of 2018, 7.8 percent.

Why was there such a precipitous drop? David Canton, a professor at Connecticut College, offers three explanations:

I look at these factors: deindustrialisation, mass incarceration, and suburbanization. With deindustrialisation — lack of tax base — we know there’s no funds to what? Construct and maintain ball fields. You see the rapid decline of the physical space in the Bronx, in Chicago, in these other urban areas, which leads to what? Lack of participation.

Suburbanization drew the tax base out of the cities. With fewer taxes in the cities, there were fewer funds to support ball fields and maintain baseball leagues.

The incarceration rates for African-American men is larger than for other demographic groups (see NCAA stats). Canton explains why that impacts participation in baseball:

I can imagine in 1980, if you were 18-year-old black man in L.A., Chicago, New York, all of a sudden, you’re getting locked up for nonviolent offenses. I’m going to assume that you played baseball. I’m arguing that those men — if you did a survey, and go to prison today, federal and state, I bet you a nice percentage of these guys played baseball. Now some were not old enough to have children. And the ones that did weren’t there to teach their son to play baseball, to volunteer in Little League because they were in jail for nonviolent offenses.

There is now a program called RBI, for Reviving Baseball in Inner cities, funded by Major League Baseball, to try to increase the participation in baseball by African-Americans and other under-served youth. There are RBI Academies in Los Angeles, New York, Kansas City, and St. Louis.

So, why are there so few African-Americans in baseball? One might assume that they just choose not to play baseball, just as how Stuart Reges decided that the lack of women in the Tech industry means that they don’t want to code.

I find the parallels between the two stories striking:

  • Baseball used to be 18.7% African-American.
  • Computer Science used to be 40% female.
  • There have been and are great African-American baseball players. (In 1981, 22% of the All-Star game rosters, were African-American, according to Forbes.) There is no inherent reason why African-Americans can’t play baseball.
  • There have been and are great female computer scientists. There is no inherent reason why women can’t code.
  • Today, baseball is only 7.8% African-American.
  • Today, computer science is only about 17% female (in undergraduate enrollment).
  • There are structural and systemic reasons why there are fewer African-Americans in baseball, such as deindustrialization, suburbanization, and a disproportionate impact of incarceration on the African-American community. (Some commentators say that the whiteness of baseball runs much deeper.)
  • There are structural and systemic reasons where there are fewer women in computer science. There are many others, like the thoughtful posts from Jen Mankoff and Ann Karlin, and the heartfelt personal blog post by Kasey Champion, who have listed these far better than I could.
  • Major League Baseball recognizes the problem and has created RBI to address it.
  • The Tech industry, NSF (e.g., through creation of NCWIT), and others recognize the problem and are working to address it. Damore and Reges are among those in Tech who are arguing that we shouldn’t be trying to address this problem, that there are differences between men and women, and that we’re unlikely to ever reach gender equity in Tech.

Maybe there are people pushing back on the RBI program in baseball, who believe that African-Americans have chosen not to play baseball. I haven’t seen or heard that.

If we accept that we ought to do something to get more African-Americans past the systemic barriers into baseball, isn’t it just as evident that we should do something to get more females into Computing?

November 26, 2018 at 8:00 am 1 comment

MIT creates a College of Computing to integrate across all disciplines

Last month, MIT announced the creation of the MIT Schwarzman College of Computing, with a $1 Billion commitment (see article here).  Below is my favorite part of the press release.  I’ll paraphrase the elements that have me excited about what MIT is going do with this new College:

  • It’s not just about taking CS to the other disciplines. It’s about “allowing the future of computing and AI to be shaped by insights from all other disciplines.”  This is key to Peter Denning’s notion of Computing and not just Computer Science.  Computing is about the rest of the world influencing, pushing, and advancing what we know about computer science.
  • The 50 new positions are going to be in the College and joint with other departments.  That’s a key step to get integration.
  • When they talk about what they’re going to do with this new College, “education” is the first word, and “research and innovation” are second and third.  Does that ordering imply a priority? Will it really keep those priorities? Who knows, but they’re good words.
  • There goal is that every student knows to “responsibly use and develop” computing technologies and AI.  Is MIT going to institute a campus-wide computing course requirement?  Even better would be to make sure that there is significant computing in the disciplinary courses.  The NYTimes article (see here) quotes MIT President Reif as aiming to “educate the bilinguals of the future.”

    He defines bilinguals as people in fields like biology, chemistry, politics, history and linguistics who are also skilled in the techniques of modern computing that can be applied to them.

Yes! That’s an exciting vision.

Headquartered in a signature new building on MIT’s campus, the new MIT Schwarzman College of Computing will be an interdisciplinary hub for work in computer science, AI, data science, and related fields. The College will:

  • reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines;

  • create 50 new faculty positions that will be located both within the College and jointly with other departments across MIT — nearly doubling MIT’s academic capability in computing and AI;

  • give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI;

  • educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world; and

  • transform education and research in public policy and ethical considerations relevant to computing and AI.

 

November 19, 2018 at 8:00 am 5 comments

How Machine Learning Impacts the Undergraduate Computing Curriculum

I’ve been looking forward to seeing this article in print since Ben Shapiro first talked about this, months and months ago. Ben, Rebecca Fiebrink, and Peter Norvig raise the (reasonable) argument that machine learning is now a central activity in computer science, and should be a core topic in undergraduate computing curriculum. What does that mean for what we teach and how we teach it? It’s something that we ought to be talking about.

The growing importance of machine learning creates challenging questions for computing education…

Changes to the Introductory Sequence…These same two aims can also describe introductory courses for an ML-as-core world. We do not envision that ML methods would replace symbolic programming in such courses, but they would provide alternative means for defining and debugging the behaviors of functions within students’ programs. Students will learn early on about two kinds of notional machine—that of the classical logical computer and that of the statistical model. They will learn methods for authoring, testing, and debugging programs for each kind of notional machine, and learn to combine both models within software systems.

We imagine that future introductory courses will include ML through the use of beginner-friendly program editors, libraries, and assignments that encourage students to define some functions using ML, and then to integrate those functions within programs that are authored using more traditional methods. For instance, students might take a game they created in a prior assignment using classical programming, and then use ML techniques to create a gestural interface (for example, using accelerometers from a smartphone, pose information from a webcam, or audio from a microphone) for moving the player’s character up, down, left, and right within that game. Such assignments would engage students in creating or curating training examples, measuring how well their trained models perform, and debugging models by adjusting training data or choices about learning algorithms and features.

 

Source: How Machine Learning Impacts the Undergraduate Computing Curriculum

November 16, 2018 at 7:00 am 4 comments

Fixing Mathematical Notation with Computing, and “Proving” It with Education

I was looking for a paper that I needed to review last night, and came upon these paragraphs in the paper I brought up by mistake.

Computers_and_Mathematical_Notation_-_Iverson_on_J

This is bold language:

It might be argued that mathematical notation (MN) is adequate as it is, and could not benefit from the infusion of ideas from programming languages. However, MN suffers an important defect: it is not executable on a computer, and cannot be used for rapid and accurate exploration of mathematical notions.

The paper I found in my archive “Computers and Mathematical Notation” doesn’t seem to be published anywhere.The author is Kenneth E. Iverson, the inventor of APL. This paper echoes some of the thoughts in Iverson’s 1980 Turing Award Lecture, “Notation as a Tool of Thought.”

The unpbulished paper is notable because he wrote it in J, his successor language to APL.  He realized that his languages would be more accessible if they used the ASCII character set. J (which you can find at http://jsoftware.com/) is essentially APL, but mapped to a normal keyboard.

The attempt to “fix” mathematical notation (“suggestions for improvement,” to be exact) is bold and interesting.  What makes his argument particularly relevant for this blog is how he made the argument. How do you “prove” that you have improved on traditional mathematics notation?

Iverson decided that education was the way to do it.  He wrote mathematics textbooks, using J.  He wanted to show that basic mathematics is more explorable using his notation.

I find this network of papers and textbooks fascinating.  I love the goal of inventing a programming notation, not to develop software, but to improve the expression and exploration of mathematics. (In that sense, J is like Mathematica.) I am intrigued by the challenge of how to show that you succeeded, and to use education as a way to demonstrate that success. I’m amazed at these multiple textbooks that Iverson wrote and released for free, to encourage exploration of mathematical ideas with J.


This week, I was informed that I will be receiving the 2019 SIGCSE Award for Outstanding Contribution to CS Education. The award will be presented at the 2019 SIGCSE Technical Symposium to be held in Minneapolis, MN  from Feb 27 – March 2, 2019. I am honored and thrilled.  SIGCSE has been my academic home since my first ACM publication at SIGCSE’94. The list of awardees is stunning, including my advisor, Elliot Soloway, Alan Kay, Hal Abelson, Jan Cuny, Alan Perlis, Judith Gal-Ezer, Sally Fincher, Grace Murray Hopper, Wirth, Knuth, and Dijkstra (among many others — the award started in 1981). It’s an impressive club I’m joining.

That announcement didn’t feel like enough for a blog post in itself, so I’m just tacking it on down here.  I’ll probably write more about it when I figure out what I’m going to say in my talk.

 

November 2, 2018 at 7:00 am 4 comments

Analyzing CS in Texas school districts: Maybe enough to take root and grow

My Blog@CACM for this month is about Code.org’s decision to shift gradually the burden of paying for CS professional development to the local regions — see link here.  It’s an important positive step that needs to happen to make CS sustainable with the other STEM disciplines in K-12 schools.

We’re at an interesting stage in CS education. 40-70% of high schools have CS, but the classes are pretty empty.  I use Indiana and Texas as examples because they’ve made a lot of their data available.  Let’s drill a bit into the Texas data to get a flavor of it, available here.  I’m only going to look at Area 1’s data, because even just that is deep and fascinating.

Brownsville Intermediate School District. 13,941 students. 102 in CS.

Computer_Science_Regional_Data___STEM_Center___The_University_of_Texas_at_Austin

Of the 10 high schools in Brownsville ISD, only two high schools have anyone in their CS classes.  Brownsville Early College High School has 102 students in CS Programming (no AP CS Level A, no AP CSP).  That probably means that one teacher has several sections of that course — that’s quite a bit.  The other high school, Porter Early College High School has fewer than five students in AP CS A.  My bet is that there is no CS teacher there, only five students doing an on-line class.  That means for 10 high schools and 13K students, there is really only one high school CS teacher.

Edinburg Consolidated Independent School District, over 10K students, 92 students in CS.

Computer_Science_Regional_Data___STEM_Center___The_University_of_Texas_at_Austin-3

This is a district that could grow CS if there was will.  There are 6 high schools, but two are special cases: One with less than 5 students, and the other in a juvenile detention center.  The other four high schools are huge, with over 2000 students each.  In Economedes, that are only 9 students in AP CS A — maybe just on-line?  Edinburg North and Robert R Vela high school each have two classes: AP CS A and CS1.  With 21 and 14, I’m guessing two sections.  The other has 43 and 6. That might be two sections of AP CS A and another of CS1, or two sections of AP CS A and 6 students in an on-line class.  In any case, this suggests two high school CS teachers (maybe three) in half of the high schools in the district.  Those teachers aren’t teaching only CS, but with increased demand and support from principals, the CS offerings could grow.

It’s fascinating to wander through the Texas data, to see what’s there and what’s not.  I could be wrong about what’s there, e.g., maybe there’s only one teacher in Edinburg and she’s moving from school-to-school.  Given these data, there’s unlikely to be a CS teacher in every high school, who just isn’t teaching any CS. These data are a great snapshot. There is CS in Texas high schools, and maybe there’s enough there to take root and grow.

 

October 19, 2018 at 7:00 am 2 comments

ECEP has a new home at The University of Texas at Austin: First meeting this week at CSforAll

I can’t tell you how exciting this press release is for me.  Rick Adrion, Renee Fall, Barbara Ericson, and I started the Expanding Computing Education Pathways Alliance (http://ecepalliance.org) in 2012 to provide states with support as they broadened participation in computing education.  Six years later, we had 16 states and Puerto Rico involved — but we were ready to be done.  We all four had worked on previous alliances (CAITE and Georgia Computes) and felt that the movement needed new leaders.  I am so very pleased that Carol Fletcher and her wonderful team decided to carry on ECEP, and NSF has agreed to continue funding ECEP as it expands to TWENTY-THREE states and US territories!

ECEP (now based out of UT-Austin) will have its first meeting this week, at Wayne State University in Detroit (where Barbara and I first met in 1983) as part of the CSforAll summit.

The National Science Foundation (NSF) has awarded the UT STEM Center a three-year $2.5 million grant to lead the Expanding Computing Education Pathways (ECEP) Alliance. ECEP is one of eight Broadening Participation in Computing Alliances (BPC) funded by the NSF to increase the number and diversity of students in K-16 pathways. ECEP works with state leadership teams to achieve this goal through education policy reform. First launched in 2012 through an NSF grant to Georgia Tech and the University of Massachusetts Amherst, ECEP has since grown through four phases from two states to sixteen and Puerto Rico. Building on the existing network of ECEP states noted in the map above, the ECEP leadership team is pleased to announce the fifth phase addition of six new states to the Alliance: Hawaii, Minnesota, Mississippi, Ohio, Oregon, and Washington.

Source: National Alliance for Expanding Computing Education Pathways has a new home at The University of Texas at Austin

October 8, 2018 at 7:00 am Leave a comment

Closing the gaps is the real challenge in computing education (CIRCL Meet Mark Guzdial)

Meet_Mark_Guzdial_–_CIRCLThe Center for Innovative Research in CyberLearning (CIRCL) did a Perspectives interview with me (thanks, Quinn Burke!) that appears here.

I got to talk about the range of things I’ve done, what I’ve been surprised by and not surprised by, and what I think the big challenges to come in K-12 CS education.

In hindsight, it’s not a surprise that we’re having trouble closing the gaps.  There are increasingly more teachers who can teach CS, and there are governors and the Tech industry pushing for more CS Ed.  But in between, there are principals that don’t buy it, and the classes in the schools are few and tiny.  Most Schools of Education are still not players in promoting CS education. I predict over 85% of kids in Georgia (at least) are not getting a single experience with CS.  The percentage of schools having CS is getting higher, but real experience with CS is low.

As you might imagine, I focus on the need for more research and for reducing inequities. We have made a lot of progress on computing education, and we can make more progress still.


N.B. as Shriram points out in the comments, our claim for FCS1 about “language independent” is really about “multi-lingual.” I’ve asked CIRCL to update the piece, and I’ll try to be more careful about what I claim for FCS1 and SCS1.

 

October 1, 2018 at 8:00 am 11 comments

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