Posts filed under ‘Uncategorized’

A high-level report on the state of computing education policy in US states: Access vs Participation

states-policyInteresting analysis from Code.org on the development of policies in US states that promote computing education — see report here, and linked below.  The map above is fascinating in that it shows how much computing education has become an issue in all but five states.

The graph below is the one I found confusing.

urm-access

I’ve been corrected: the first bar says that where the school’s population is 0-25% from under-represented minority groups, 41% of those schools teach CS.  Only 27% of mostly-minority schools (75%-100% URM, in the rightmost column) offer CS.  This is a measure of which schools offer computer science.

The graph above doesn’t mean that there are any under-represented minority students in any CS classes in any of those high schools.  My children’s public high school in Georgia was over 50% URM, but the AP CS class was 90% white and Asian kids.  From the data we’ve seen in Georgia (for example, see this blog post), few high schools offer more than one CS class. Even in a 75% URM high school, it’s pretty easy to find 30 white and Asian guys.  Of course, we know that there are increasing numbers of women and under-represented minority students in computer science classes, but that’s a completely different statistic from what schools offer CS.

I suspect that the actual participation of URM students in CS is markedly lower than the proportion in the school.  In other words, in a high school with 25% URM, I’ll bet that the students in the CS classes are less than 25% URM.  Even in a 75% URM high school, I’ll bet that CS participation is less than 75% URM.

Access ≠ participation.

Source: The United States for Computer Science – Code.org – Medium

October 12, 2018 at 7:00 am 5 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

Preparing students for a research career: Gregory Abowd’s 30 PhD Graduates

Georgia Tech’s School of Interactive Computing did an article on my friend Gregory Abowd and his 30 PhD graduates, many of whom have continued in academia. You can find the article here.

The “Abowd family” is a real thing. The article ends talking about how Gregory and his students and their students get together at conferences. I’ve seen pictures of these events. There’s a strong sense of kinship and support in the group, inspired by Gregory.

Here at the University of Michigan, we have just hired two second-generation members of the Abowd family. Gabriela Marcu (see webpage here) and Nikola Banovic (see webpage here) both earned their PhD’s at CMU, working with former Gregory students Jen Mankoff and Anind Dey (who have now moved to U. Washington).  What’s striking to me about both Gabriela and Nikola is that they started down the path to academic research by doing undergraduate research with other Abowd graduates: Gillian Hayes at Irvine and Khai Troung at Toronto (respectively).

What does it take to support future academic researchers while they are still undergraduates?  Obviously, we don’t want all of our undergraduates to become researchers. But we need some. Academic researchers in computing perform a useful and important role. We particularly want more women getting into computing research, and kudos to Google for awarding fifteen grants to promote more women getting into computing research (see article here). We do not have enough CS academics today (as I described in this blog post), and that’s part of the struggle in dealing with the enrollment boom. So we want more — how do we get them?  What do we do at the undergraduate level to make it more likely that we get graduates like Gabriela and Nikola?

We need to expect that CS undergraduates will have careers other than software developers. We often build our undergraduate programs assuming that all of our graduates will become software developers, or will manage software developers. But you can do a lot with a CS degree. We have to build into our programs the features that will help students succeed in the career that they choose, including becoming academic researchers.

One of my colleagues in the Engineering Education Research program here, Joi Mondisa, researches mentoring. She just gave the first EER Seminar, and talked about the importance of being “treated/advised like family.”  Mentors give their mentees honest and valuable advice as if the mentee were a family member.

I suspect that that’s part of Gregory’s success — that the notion of being in the “Abowd family” is something that the members feel and actively participate in. That’s likely a lesson that we can use in the future. Personal mentoring relationships play a big role in encouraging future researchers.  I don’t know how to build personal “like family” research relationships into an undergraduate program, especially at the enrollment scales we see today. But it’s an important problem to think about, both because we should support a variety of outcomes for our CS undergraduates and because one way of managing the enrollment crisis is to grow more CS faculty.

 

September 28, 2018 at 7:00 am 3 comments

The Backstory on Barbie the Robotics Engineer: What might that change?

Professor Casey Fiesler has a deep relationship with Barbie, that started with a feminist remix of a book.  I blogged about the remix and Casey’s comments on Barbie the Game Designer in this post. Now, Casey has helped develop a new book “Code Camp with Barbie and Friends” and she wrote the introduction. She tells the backstory in this Medium blog post.

In her essay, Casey considers her relationship with Barbie growing up:

I’ve also thought a lot about my own journey through computing, and how I might have been influenced by greater representation of women in tech. I had a lot of Barbies when I was a kid. For me, dolls were a storytelling vehicle, and I constructed elaborate soap operas in which their roles changed constantly. Most of my Barbies dated MC Hammer because my best friend was a boy who wasn’t allowed to have “girl” dolls, and MC was way more interesting than Ken. I also wasn’t too concerned about what the box told me a Barbie was supposed to be; otherwise I’d have had to create stories about models and ballerinas and the occasional zookeeper or nurse. My creativity was never particularly constrained, but I can’t help but think that even just a nudge — a reminder that Barbie could be a computer programmer instead of a ballerina — would have influenced my own storytelling.

I’ve been thinking about how Barbie coding might influence girls’ future interest in Tech careers.  I doubt that Barbie is a “role model” for many girls. Probably few girls want to grow up to be “like Barbie.” What a coding Barbie might do is to change the notion of “what’s acceptable” for girls.

In models of how students make choices in academia (e.g., Eccles’ expectancy-value theory) and how students get started in a field (e.g., Alexander’s Model of Domain Learning), the social context of the decision matters a lot. Students ask themselves “Do I want to do this activity and why?” and use social pressure and acceptance to decide what’s an appropriate class to take.  If there are no visible girls coding, then there is no social pressure. There are no messages that programming is an acceptable behavior.  A coding Barbie starts to change the answer to the question, “Can someone like me do this?”

September 24, 2018 at 7:00 am 2 comments

Why Don’t Women Want to Code? Better question: Why don’t women choose CS more often?

Jen Mankoff (U. Washington faculty member, and Georgia Tech alumna) has written a thoughtful piece in response to the Stuart Reges blog post (which I talked about here), where she tells her own stories and reframes the question.

Foremost, I think this is the wrong question to be asking. As my colleague Anna Karlin argues, women and everyone else should code. In many careers that women choose, they will code. And very little of my time as an academic is spent actually coding, since I also write, mentor, teach, etc. In my opinion, a more relevant question is, “Why don’t women choose computer science more often?”

My answer is not to presume prejudice, by women (against computer science) or by computer scientists (against women). I would argue instead that the structural inequalities faced by women are dangerous to women’s choice precisely because they are subtle and pervasive, and that they exist throughout a woman’s entire computer science career. Their insidious nature makes them hard to detect and correct.

Source: Why Don’t Women Want to Code? Ask Them! – Jennifer Mankoff – Medium

September 21, 2018 at 7:00 am 2 comments

International effort to improve data science in schools

I’ve been involved in this project over the last few months. (Where “involved” means, “a couple of phone conversations, and a set of emails about frameworks, standards, and curricula, and I missed every physical meeting.”) Nick Fisher has drawn together an impressive range of experts and professional societies to back the effort. It’s not clear where it’s going, but it is indicative of a growing worldwide interest in “data science” in schools.

The definition of “data science” is fuzzy for me, almost as fuzzy as the term “computational thinking.”  Does data science include computer science? statistics? probability? I think the answer is “yes” to all of those, but then it might be too big to easily teach in secondary schools. If we’re struggling to teach CS to teachers, how do we teach them CS and statistics and probability?

And if budgets and schedules are are a zero-sum game, what do we give up in order to teach data science?  For example, teacher preparation programs are packed full. What do we not teach in order to teach teachers about data science?

This group of experts knows a lot about what works in data science. Their opinion on what students need to know creates a useful measuring stick with which to look at the several data science classes that are being created (such as Unit 5 in Exploring CS). There’s some talk about this group of experts might develop their own course. I’m not sure that it’s possible to create a course to work internationally — school systems and expectations vary dramatically. But a framework is useful.

The aim of the International Data Science in Schools Project (IDSSP) is to transform the way data science is taught the last two years of secondary school. Its objectives are:

1. To ensure that school children develop a sufficient understanding and appreciation of how data can be acquired and used to make decisions so that they can make informed judgments in their daily lives, as children and then as adults

2. To inspire mathematically able school students to pursue tertiary studies in data science and its related fields, with a view to a career.

“In both cases, we want to teach people how to learn from data,” Dr Fisher said.

Two curriculum frameworks are being created to support development of a pre-calculus course in data science that is rigorous, engaging and accessible to all students, and a joy to teach.

  • Framework 1 (Data Science for students). This framework is designed as the basis for developing a course with a total of some 240 hours of instruction.
  • Framework 2 (Data Science for teachers). As a parallel development, this framework is designed as the basis for guiding the development of teachers from a wide variety of backgrounds (mathematics, computer science, science, economics, …) to teach a data science course well.

Dr Fisher said that the draft frameworks will be published for widespread public consultation in early 2019 before completion by August.

“We envisage the material will be used not just in schools, but also as a valuable source of information for data science courses in community colleges and universities and for private study.” For further information: idssp.info@gmail.com, or visit www.idssp.org

September 17, 2018 at 7:00 am 2 comments

Older Posts


Enter your email address to follow this blog and receive notifications of new posts by email.

Join 5,326 other followers

Feeds

Recent Posts

Blog Stats

  • 1,562,298 hits
October 2018
M T W T F S S
« Sep    
1234567
891011121314
15161718192021
22232425262728
293031  

CS Teaching Tips