ICER 2021 Preview: The Challenges of Validated Assessments, Developing Rich Conceptualizations, and Understanding Interest #icer2021

The International Computing Education Research Conference (ICER) 2021 is this week (website here). It should have been in Charleston, South Carolina (one of my favorite cities), but it will instead be all on-line. Unlike previous years, if you are not already registered, you’re unfortunately out of luck. As seen in Matthias Hauswirth’s terrific guest blog post from last week (see here), getting set up in Clowdr is complicated. ICER won’t have the resources to bring people on-line and get them through the half hour prep sessions on-the-fly. There will be no “onsite” registration.

However, all the papers should be available in the ACM Digital Library (free for some time), and I think all the videos of the talks will be made available after the fact, so you can still gain a lot from the conference. Let me point out a few of the highlights that I’m excited about. (As of this writing, the papers are not yet appearing in the ACM DL — all the DOI links are failing for me. I’ll include the links here in hopes that everything is fixed soon.)

Our keynoter is Tammy Clegg, whom I got to know when she was a PhD student at Georgia Tech. She’s now at U. Maryland doing amazing work around computation and relevant science learning. I’m so looking forward to hearing what she has to say to the ICER community.

Miranda Parker, Allison Elliott Tew, and I have a paper “Uses, Revisions, and the Future of Validated Assessments in Computing Education: A Case Study of the FCS1 and SCS1.” This is a paper that we planned to write when Miranda first developed the SCS1 (first published in 2016). We created the SCS1 in order to send it out to the world for use in research. We hoped that we could sometime later do in CS what Richard Hake did in Physics, when he used the FCI to make some strong statements about teaching practices with a pool of 6,000 students (see paper here). Hake’s paper had a huge impact, as it started making the case to shift from lecture to active learning. Could we use the collected use of the SCS1 to make some strong arguments for improving CS learning? We decided that we couldn’t. The FCI was used in pretty comparable situations, and it’s tightly focused on force. CS1 is far too broad, and FCS1 and SCS1 are being used in so many different places — not all of which it’s been validated for. Our retrospective paper is kind of a systemic literature review, but it’s done from the perspective of tracing these two instruments and how they’ve been used by the research community.

One of the papers that I got a sneak peek at was “When Wrong is Right: The Instructional Power of Multiple Conceptions” by Lauren Margulieux, Paul Denny, Katie Cunningham, Mike Deutsch, and Ben Shapiro. The paper is exploring the tensions between direct instruction and more student-directed approaches (like constructionism and inquiry learning) (see a piece I did in 2015 about these tensions). The basic argument of this new paper is that just telling students the right answer is not enough to develop rich understanding. We have to figure out how to help students to be able to hold and compare multiple conceptions (not all of which is canonical or held by experts), so that they can compare and contrast, and use the right one at the right time.

I’m chair for a session on interest. While I haven’t seen the papers yet, I got to watch the presentations (which are already loaded in Clowdr). “Children’s Implicit and Explicit Stereotypes on the Gender, Social Skills, and Interests of a Computer Scientist” by de Wit, Hermans, and Aivaloglou is a report on a really interesting experiment. They look at how kids associate gender with activities (e.g., are boys more connected to video games than girls?). The innovative part is that they asked the questions and timed the answers. A quick answer likely connects to implicit beliefs. If they take a long time to answer, maybe they told you what they thought you wanted to hear? The second paper “All the Pieces Matter: The Relationship of Momentary Self-efficacy and Affective Experiences with CS1 Achievement and Interest in Computing” by Lishinski and Rosenberg asks about what leads to students succeeding and wanting to continue in computing. They look at students affective state coming into CS1 (e..g, how much do they like computing? How much do they think that they can succeed in computing?), and relate that to students’ experiences and affective state after the class. They make some interesting claims about gender — that gender gaps are really self-efficacy gaps.

One of the more unusual sessions is a pair of papers from IT University of Copenhagen that make up a whole session. ICER doesn’t often give over a whole session to a single research group on multiple papers. One is “Computing Educational Activities Involving People Rather Than Things Appeal More to Women (Recruitment Perspective)” and the other is “Computing Educational Activities Involving People Rather Than Things Appeal More to Women (CS1 Appeal Perspective).” The pitch is that framing CS1 as being about people rather than things leads to better recruitment (first paper) and more success in CS1 (second paper) in terms of gender diversity. It’s empirical support for a hypothesis that we’ve heard before, and the authors frame the direction succinctly: “CS is about people not things.” Is that succinct enough to get CS faculty to adopt this and teach CS differently?

Why aren’t more girls in the UK choosing to study computing and technology? Guest blog post by Peter Kemp

The Guardian raised the question in the title in this article in June. Pat Yongpradit sent it to me and Peter Kemp, and Peter’s response was terrific — insightful and informed by data. I asked him if I could share it here as a guest post, and he graciously agreed.

We’ve just started a 3 year project, scaricomp, that aims to look at girls’ performance and participation in computer science in English schools. There’s not much to see at the moment, as we started in April, but we’re hoping to sample 5000+ students across schools with large numbers of students taking CS and/or high numbers of females in the CS cohorts. I’ll let you know when we have some analysis in hand.

You reference The Guardian article’s quote: “In 2019, 17,158 girls studied computer science, compared with the 20,577 girls who studied ICT in 2018”. It’s worth noting that the 2018 ICT figure was the end of the line for ICT, numbers in previous years were much higher, and the female figure was actually ~40% of the overall ICT entries, whilst it represents about 20% of the GCSE CS cohort, i.e. females were proportionally better represented in ICT than CS. For a fuller picture of the changing numbers and demographics in English computing, see slide 8 of this, or the video presentation). It’s also worth noting that since the curriculum change in 2012/13 we’ve lost the majority of time dedicated to teaching computing (including CS) at age 14-16, I’ve argued that this has had a disproportionate impact on girls and poorer students (page 45-48).

To add a bit of context from England: Students typically pick 8-10 subjects for GCSE, though their ‘options’ might be limited. Most schools will insist that students take Maths, English Language, English Literature, Physics, Chemistry, Biology, and often: French or German, and History or Geography. This leaves students with one or two actual ‘options’. Many schools are also imposing entry requirements on GCSE CS, only letting the high achieving students (often focusing on maths) onto the course; this will likely have an impact in access to the curriculum for poorer students who are less likely to achieve well in mathematics. Why don’t females pick CS in the same way they picked ICT? This might well be linked to curriculum, role models, contextualisation etc.

One of the reasons given for the curriculum change in 2012 was that students were being “bored to death” by ICT, with ICT generally being the application of software products to solve problems and the implication of technology on the world. The application of technology to the world lends itself to the contextualisation of the curriculum and the assessment materials. There was a lot of project-based assessment with real world scenarios for students to engage with, e.g. making marketing materials for businesses, using spreadsheets to organise holiday bookings etc https://web.archive.org/web/20161130183550if_/http://www.aqa.org.uk/subjects/computer-science-and-it/gcse/information-and-communication-technology-4520) . The GCSE CS is a different beast. It can be contextualised, but this is probably more difficult to do as there is an awful lot of material to cover and the assessment methodology is entirely exam based and on paper for the largest exam boards. Anecdotally we hear of schools cutting down on programming time on computers, as the exam is handwritten.

Data looking at what females ‘liked’ in the old ICT curriculum is quite limited, but what does exist places some of the ‘non-CS’ elements quite highly. So, the actual curriculum content might have a part to play here. Having taught ICT (and CS) for many years, most students I knew really enjoyed the ICT components. I’d argue that the pre-reform discourse around ICT being: “useless, boring, easy”, CS being: “useful, exciting, rigorous” was an easy political position to take, and not reflective of reality where schools had competent teachers. We now find ourselves in a position where we probably have a little too much CS, and not enough digital literacy / ICT for the general needs of students. I and people like Miles Berry (p49) have argued for more generalist qualification which maintains elements of CS. Though there appears to be little political will to make this happen.

To add another suggestions as to why we’re seeing females disengaging, within the English context, we see females substantially underachieving at GCSE in comparison to their other subjects and males of similar ‘abilities’ (ability here being similar grade profiles in other subjects). Why this is remains unclear, we see similar under achievement in Maths and Physics. My fear is that encouraging females to take CS might lead them to having their self-efficacy knocked and therefore make them less likely to pursue further study or a career in tech. We also found that females from poorer backgrounds were more likely to pick GCSE CS than their middle-class peers, we speculate that this might be the result of different cultural/family pressures and a keener engagement with the ’employability’ and ‘good pay’ discourse that often surrounds the representation of studying CS, however true this might be for these groups in reality. More research on the above coming soon through scaricomp.

Additionally, in terms of the UK picture, you’ll probably want to check in with Sue Sentance and the Gender Balance in Computing Project. One of their theories for the decline in computing is that CS is being timetabled at the same time as other (generally) more attractive subjects for females. I’m not sure if they’ve started this part of the research yet, but it’s worth checking in. They are running interventions across the country, but I don’t believe that they are trying to do a nationally representative survey.

Why some students do not feel that they belong in CS, and how we can encourage the sense that they do belong

One of my favorite papers at ICER 2019 was by Colleen Lewis and her colleagues, and is available on her website. I’ll quote her first:

Does a match between a students’ values of helping society and their perception of computing matter? Yes! A mismatch between a students’ goals of helping society and their perception of computing predicts a lower sense of belonging. And students from groups who – on average – are more likely to want to help society (women, Black students, Latinx students, and first-generation college students), this may be particularly problematic! (pdf)

• Lewis, C. M., Bruno, P., Raygoza, J., & Wang, J. (2019). Alignment of Goals and Perceptions of Computing Predicts Students’ Sense of Belonging in Computing.Proceedings of the International Computer Science Education Research Workshop. Toronto, Canada.

I want to expand a bit on that paragraph. I often get the question, “Why aren’t more women and URM students going into CS?” We’re seeing female students and students of color leaving/avoiding CS at many stages, e.g., Barb’s deep analysis of AP CS*. Colleen and her collaborators are giving us one answer.

We know that students who have a sense of belonging in computing are more likely to stay in computing. Colleen et al. found that students who found that their values were supported in computing were more likely to feel a sense of belonging. So, if what you want to do with your life matches computing, you’re more likely to stick around in computing. This is the “alignment of goals” and “perceptions of computing” part of the title.

Next step: Students from demographic groups underrepresented in computing were more likely to value community and helping society than other students. These are their goals. Do students see that their goals align with their perception of computing? If so, then you have an increased sense of belonging. Colleen and her colleagues found that If the students who valued community perceived that they could use computing to support communal values, then they were more likely to stick around.

This result is obviously explanatory. It helps us to understand who stays in computing. It also suggests interventions. Want to retain more under-represented students in your CS classes? Help them to see that they can pursue their values in computing. Help them to update their perceptions so that they see the alignment of their goals with computing goals.

But what if you (as the teacher) don’t? This paper suggests future research questions. What if your CS class is entirely de-contextualized and doesn’t say anything about what the students might do with computing? What perceptions do the students bring to the CS class if nobody helps them to see the possibilities in computing? Which student goals align with these perceived goals of computing? We might guess what the answers might be, but it really does call for some explicit research. What are students’ goals and perceptions of computing in most CS classes today?

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* Check out Barb’s blog at https://cs4all.home.blog/. As I’m writing this, Barb is finishing up the 2019 AP analysis. The gap between white and Black student pass rates on AP CSP is enormous, far larger than the gap on AP CS A. I’m hoping that she has updates there by the time this post appears.

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.

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.

 

 

 

The gender imbalance in AI is greater than in CS overall, and that’s a big problem

My colleague, Rada Mihalcea, sent me a copy of a new (April 2019) report from the AI Now Institute on Discriminating Systems: Gender, Race, and Power in AI (see link here) which describes the diversity crisis in AI:

There is a diversity crisis in the AI sector across gender and race. Recent studies found only 18% of authors at leading AI conferences are women, and more than 80% of AI professors are men. This disparity is extreme in the AI industry: women comprise only 15% of AI research staff at Facebook and 10% at Google. There is no public data on trans workers or other gender minorities. For black workers, the picture is even worse. For example, only 2.5% of Google’s workforce is black, while Facebook and Microsoft are each at 4%. Given decades of concern and investment to redress this imbalance, the current state of the field is alarming.

Without a doubt, those percentages do not match the distribution of gender and ethnicity in the population at large. But we already know that participation in CS does not match the population. How do the AI distributions match the distribution of gender and ethnicity among CS researchers?

A sample to compare to is the latest graduates with CS PhDs. Take a look at the 2018 Taulbee Survey from the CRA (see link here).  19.3% of CS PhD’s went to women. That’s terrible gender diversity when compared to the population, and AI  (at 10%, 15%, or 18%) is doing worse. Only 1.4% of new CS PhD’s were Black. From an ethnicity perspective, Google, Facebook, and Microsoft are doing surprisingly well.

The AI Now Institute report is concerned about intersectionality. “The overwhelming focus on ‘women in tech’ is too narrow and likely to privilege white women over others.” I heard this concern at the recent NCWIT Summit (see link here).  The issues of women are not identical across ethnicities. The other direction of intersectionality is also a concern. My student, Amber Solomon, has published on how interventions for Black students in CS often focus on Black males: Not Just Black and Not Just a Woman: Black Women Belonging in Computing (see link here).

I had not seen previously a report on diversity in just one part of CS, and I’m glad to see it. AI (and particularly the sub-field of machine learning) is growing in importance. We know that having more diversity in the design team makes it more likely that a broader range of issues are considered in the design process. We also know that biased AI technologies are already being developed and deployed (see the Algorithmic Justice League). A new Brookings Institute Report identifies many of the biases and suggests ways of avoiding them (see report here). AI is one of the sub-fields of computer science where developing greater diversity is particularly important.

 

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?

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?”

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

US National Science Foundation increases emphasis on broadening participation in computing

The computing directorate at the US National Science Foundation (CISE) has increased its emphasis on broadening participation in computing (BPC).  (See quote below and FAQ here.) They had a pilot program where large research grants were required to include a plan to increase the participation of groups or populations underrepresented or under-served in computing. They are now expanding the program to include medium and large scale grants. The idea is to get more computing researchers nationwide focusing on BPC goals.

CISE recognizes that BPC requires an array of long-term, sustained efforts, and will require the participation of the entire community. Efforts to broaden participation must be action-oriented and must take advantage of multiple approaches to eliminate or overcome barriers. BPC depends on many factors, and involves changing culture throughout academia—within departments, classrooms, and research groups. This change begins with enhanced awareness of barriers to participation as well as remedies throughout the CISE community, including among principal investigators (PIs), students, and reviewers. BPC may therefore involve a wide range of activities, examples of which include participating in professional development opportunities aimed at providing more inclusive environments, joining various existing and future collective impact programs to helping develop and implement departmental BPC plans that build awareness, inclusion, and engagement, and conducting outreach to underrepresented groups at all levels (K-12, undergraduate, graduate, and postgraduate).

In 2017, CISE commenced a pilot effort to increase the community’s involvement in BPC, by requiring BPC plans to be included in proposals for certain large awards [notably proposals to the Expeditions in Computing program, plus Frontier proposals to the Cyber-Physical Systems and Secure and Trustworthy Cyberspace (SaTC) programs]. By expanding the pilot to require that Medium and Large projects in certain CISE programs [the core programs of the CISE Divisions of Computing and Communication Foundations (CCF), Computer and Network Systems (CNS), and Information and Intelligent Systems (IIS), plus the SaTC program] have approved plans in place at award time in 2019, CISE hopes to accomplish several things:

• Continue to signal the importance of and commitment to BPC;
• Stimulate the CISE community to take action; and
• Educate the CISE community about the many ways in which members of the community can contribute to BPC.

The long-term goal of this pilot is for all segments of the population to have clear paths and opportunities to contribute to computing and closely related disciplines.

Read more at https://www.nsf.gov/pubs/2018/nsf18101/nsf18101.jsp

High school students learning programming do better with block-based languages, and the impact is greatest for female and minority students

I learned about this study months ago, and I was so glad to see it published in ICLS 2018 this last summer.  The paper is “Blocks or Text? How Programming Language Modality Makes a Difference in Assessing Underrepresented Populations” by David Weintrop, Heather Killen, and Baker Franke.  Here’s the abstract:

Broadening participation in computing is a major goal in contemporary computer science education. The emergence of visual, block-based programming environments such as Scratch and Alice have created a new pathway into computing, bringing creativity and playfulness into introductory computing contexts. Building on these successes, national curricular efforts in the United States are starting to incorporate block-based programming into instructional materials alongside, or in place of, conventional text-based programming. To understand if this decision is helping learners from historically underrepresented populations succeed in computing classes, this paper presents an analysis of over 5,000 students answering questions presented in both block-based and text-based modalities. A comparative analysis shows that while all students perform better when questions are presented in the block-based form, female students and students from historically underrepresented minorities saw the largest improvements. This finding suggests the choice of representation can positively affect groups historically marginalized in computing.

Here’s the key idea as I see it. They studied over 5,000 high school students learning programming. They compared students use block-based and text-based programming questions.  Everyone does better with blocks, but the difference is largest for female students and those from under-represented groups.

Here’s the key graph from the paper:

So, why wouldn’t we start teaching programming with blocks?  There is an issue that students might think that it’s a “toy” and not authentic — Betsy DiSalvo saw that with her Glitch students. But a study with 5K students suggests that the advantages of blocks swamp the issues of inauthenticity.

The International Conference on the Learning Sciences (ICLS) 2018 Proceedings are available here.

SIGCSE 2018 Preview: Black Women in CS, Rise Up 4 CS, Community College to University CS, and Gestures for Learning CS

While I’m not going to be at this year’s SIGCSE, we’re going to have a bunch of us there presenting cool stuff.

On Wednesday, Barb Ericson is going to this exciting workshop, CS Education Infrastructure for All: Interoperability for Tools and Data Analytics, organized by Cliff Shaffer, Peter Brusilovsky, Ken Koedinger, and Stephen Edwards. Barb is eager to talk about her adaptive Parsons Problems and our ebook work.

My PhD student, Amber Solomon, is presenting at RESPECT 2018 (see program here) on a paper with Dekita Moon, Amisha Roberts, and Juan Gilbert, Not Just Black and Not Just a Woman: Black Women Belonging in Computing. They talk about how expectations of being Black in CS and expectations as a woman in CS come into conflict for the authors.

On Thursday, Barb is presenting her paper (with Tom McKlin) Helping Underrepresented Students Succeed in AP CSA and Beyond, which are the amazing results from the alumni study from her Project Rise Up effort to help underrepresented students succeed at Advanced Placement CS A. When Barb was deciding on her dissertation topic, she considered making Rise Up her dissertation topic, or adaptive Parsons problems. She decided on the latter, so you might think about this paper as the dissertation final chapter if she had made Rise Up her dissertation focus. Project Rise Up grew from Barb’s interest in AP CS A and her careful, annual analysis of success rates in AP CS A for various demographics (here is her analysis for 2017). It had a strong impact (and was surprisingly inexpensive), as seen in the follow-on statistics and the quotes from the students now years after Rise Up. I recommend going to the talk — she has more than could fit into the paper.

On Friday, my PhD student, Katie Cunningham, is presenting with her colleagues from California State University Monterey Bay and Hartnell College, Upward Mobility for Underrepresented Students: A Model for a Cohort-Based Bachelor’s Degree in Computer Science.  The full author list is Sathya Narayanan, Katie, Sonia Arteaga, William J. Welch, Leslie Maxwell, Zechariah Chawinga, and Bude Su. They’re presenting the “CSin3” program which drew in students from traditionally underrepresented groups and helped them earn CS degrees with remarkable success: A three year graduation rate of 71%, compared to a 22% four-year graduation rate, as well as job offers from selective tech companies. The paper describes the features of the program that made it so successful, like its multi-faceted support outside the classroom, the partnership between a community college and a university, and keeping a cohort model. The paper has been recognized with a SIGCSE 2018 Best Paper Award in the Curricula, Programs, Degrees, and Position Papers track.

On Friday, my colleague Betsy DiSalvo is going to present at the NSF Showcase some of the great work that she and her student, Kayla des Portes, have been doing with Maker Oriented Learning for Undergraduate CS.

On Saturday, my EarSketch colleagues are presenting their paper: Authenticity and Personal Creativity: How EarSketch Affects Student Persistence with Tom McKlin, Brian Magerko, Taneisha Lee, Dana Wanzer, Doug Edwards, and Jason Freeman.

Also on Saturday, Amber with her undergraduate researchers, Vedant Pradeep and Sara Li, are presenting a poster which is also a data collection activity, so I hope that many of you will stop by. Their poster is The Role of Gestures in Learning Computer Science. They are interested in how gesture can help with CS learning and might be an important evaluation tool — students who understand their code, tend to gesture differently when describing their code than students who have less understanding. They want to show attendees what they’ve seen, but more importantly, they want feedback on the gestures they’ve observed “in the wild.” Have you seen these? Have you seen other gestures that might be interesting and useful to Amber and her team? What other kinds of gestures do you use when explaining CS concepts? Please come by and help inform them about the gestures you see when teaching and learning CS.

Why Tech Leadership May Have a Bigger Race Than Gender Problem

The Wired article linked below suggests that race is an even bigger issue than gender in Tech industry leadership.  While Asians are over-represented in the Tech labor force, they are under-represented in Tech leadership, even more than women.  I was somewhat surprised that this article considers “Asians” so generally.  The most-often visited blog post I’ve written is the one that shows the differential success rates of different Asian populations in US educational attainment (see post here).

Gee says this study came about because an earlier report in 2015 that used EEOC data from companies like Google and LinkedIn ended up on the desk of Jenny Yang, the outgoing commissioner of the EEOC. Yang asked if the lower proportion of Asian executives was the result of discrimination and might be applicable for lawsuits, Gee says. He told her no. “We have never seen any overt discrimination or policies that create these disparities,” Gee explains. Rather, after conversations with 60 or 70 Asian executives, the authors say they noticed a pattern of cultural traits among some Asians that did not align with leadership expectations in Western corporate culture, such as risk-taking and being confrontational.

Gee gave the example of an executive who started the first Asian affinity group at Intel decades ago. He noticed that Chinese engineers were unhappy and not succeeding in Intel’s culture of “constructive conflict,” which involved heated debates during meetings.

“Some people call it unconscious bias. For Asians, it’s actually a very conscious bias,” says Gee. Studies show that assumptions that Asians are good at math, science, and technology make it easier for them to get in the door, but the same bias is reversed when it comes to leadership roles, he says.

Source: Why Tech Leadership May Have a Bigger Race Than Gender Problem | WIRED

Study says multiple factors work together to drive women away from STEM

I wrote recently in a blog post that we don’t know enough why women aren’t going into computing, and I wrote in another blog post that CRA is finding that we lose women over the years of an undergraduate degree in CS.  Here’s an interesting study offering explanations for why we are not getting and keeping women:

The study analyzed a large, private university on the East Coast, using data from 2009-16, broken down semester-by-semester to track students’ changes in grades and majors in as close to real time as possible. While other studies have suggested that women came out of high school less prepared, or that increasing female STEM faculty could help provide women mentors, the Georgetown study didn’t support those findings.

“Women faculty don’t seem to attract more women into a field, and that was sort of sad news for us,” Kugler said. “We were hoping we could make more of a difference.”

One of the reasons women might feel undue pressure in STEM fields might actually be because of how recruiting and mentoring is framed. Many times, those efforts actually end up reinforcing the idea that STEM is for men.“Society keeps telling us that STEM fields are masculine fields, that we need to increase the participation of women in STEM fields, but that kind of sends a signal that it’s not a field for women, and it kind of works against keeping women in these fields,” Kugler said.

And while many STEM majors are male-dominated, the framing of recruitment and mentorship efforts can sometimes paint inaccurate pictures for STEM fields that aren’t male-dominated, and contribute to an inaccurate picture for STEM as a whole, the paper says:

While men may not have a natural ability advantage in STEM fields, the numerous government and other policy initiatives designed to get women interested in STEM fields may have the unintended effect of signaling to women an inherent lack of fit.

While computer science, biophysics and physics tend to be male-dominated, Kugler said, neurobiology, environmental biology and biology of global health tend to be female-dominated.

Source: Study says multiple factors work together to drive women away from STEM

The state of women in computer science: An investigative report, featuring Barbara Ericson

The new TechRepublic report on women in computing is short but touches on a lot of important themes. Barb Ericson figures prominently in the report.

At Georgia Tech, every student is required to take one of three computer science intro courses: One for engineering majors, one for computer science majors, and one for all other students.

In the past, computer science was not taught in a very interesting way, Ericson said. And getting professors to change their habits after so much time proved difficult, she added.

Further, “a lot of instructors believe in the ‘geek gene’—that you’re born to do it or you’re not, and they often think women are not,” Ericson said. “Women can face an uphill climb from some of their professors or friends or family who are like, ‘Wait, what? Why are you doing this?'”

Intro courses should be interesting, creative, and social, and offer plenty of help, especially for women who tend to come in with less experience and less confidence, Ericson said.

Source: The state of women in computer science: An investigative report – TechRepublic