[Edit: The conference is now being held in Hong Kong. I don’t know the reason behind the change but the original issue has been addressed. I have been accepted to Learning @ Scale so will not be able to attend anyway, as it turns out, as the two conferences overlap by two days and even I can’t be in the US and Hong Kong at the same time.]
There is a large amount of discussion in the CS Ed community right now over the LATICE 2017 conference, which is going to be held in a place where many members of the community will be effectively reduced to second-class citizenship and placed under laws that would allow them to be punished for the way that they live their lives. This affected group includes women and people who identify with QUILTBAG (“Queer/Questioning, Undecided, Intersex, Lesbian, Trans (Transgender/Transsexual), Bisexual, Asexual, Gay”). Conferences should be welcoming. This is not a welcoming place for a large percentage of the CS Ed community.
There are many things I could say here but what I would prefer you to do is to look at who is commenting on this and then understand those responses in the context of the author. For once, it matters who said what, because not everyone will be as affected by the decision to host this conference where it is.
From what I’ve seen, a lot of men think this is a great opportunity to do some outreach. A lot has been written, predominantly by men, about how every place has its problems and so on and so forth.
But let’s look at other voices. The female and QUILTBAG voices do not appear to share this support. Asking for their rights to be temporarily reduced or suspended for this ‘amazing opportunity’ is too much to ask. In response, I’ve seen classic diminishment of genuine issues that are far too familiar. Concerns over the reductions of rights are referred to as ‘comfort zone’ issues. This is pretty familiar to anyone who is actually tracking the maltreatment and reduction of non-male voices over time. You may as well say “Stop being so hysterical” and at least be honest and own your sexism.
Please go and read through all of the comments and see who is saying what. I know what my view of this looks like, as it is quite clear that the men who are not affected by this are very comfortable with such a bold quest and the people who would actually be affected are far less comfortable.
This is not a simple matter of how many people said X or Y, it’s about how much discomfort one group has to suffer that we take their concerns seriously. Once again, it appears that we are asking a group of “not-men”, in a reductive sense, to endure more and I cannot be part of that. I cannot condone it.
I will not be going. I will have to work out if I can cite this conference, given that I can see that it will lead to discrimination and a reduction of participation over gender and sexuality lines, unintentionally or not. I have genuine ethical concerns about using this research that I would usually reserve for historical research. But that is for me to worry about. I have to think about my ongoing commitment to this community.
But you shouldn’t care what I think. Go and read what the people who will be affected by this think. For once, please try to ignore what a bunch of vocal guys want to tell you about how non-male groups should be feeling.
Given how much research there is to tell us that intrinsic bias is having a terrifying effect on hiring decisions and opportunity, it’s great to have some good news to report when a company decides to address the issue.
Deloitte U.K are going to start using a school-blind approach in its hiring process (WaPo link), which will potentially benefit thousands of school- and college-leavers who would have been perceived to come from “less suitable” educational pedigrees. They are also planning to work out how students performed relative to their peers in a given school, rather than against broad district or national levels. Deloitte are now part of a growing group of companies who have moved beyond “Am I getting the best people” to understanding that their biassed perception of best may be preventing them from seeing some candidates at all. They could be picking their “best” from a much broader range of better, if only they’d address those biases.
This is certainly not going to end bias overnight (there are so many other issues to address) but it’s a great start.
This sessions was dedicated to the very important issues of gender and diversity. The opening talk in this session was “A Historical Examination of the Social Factors Affecting Female Participation in Computing”, presented by Elizabeth Patitsas (@patitsel). This paper was a literature review of the history of the social factors affecting the old professional association of the word “computer” with female arithmeticians to today’s very male computing culture. The review spanned 73 papers, 5 books, 2 PhD theses and a Computing Educators Oral History project. The mix of sources was pretty diverse. The two big caveats were that it only looked at North America (which means that the sources tend to focus on Research Intensive universities and white people) and that this is a big picture talk, looking at social forces rather than individual experiences. This means that, of course, individuals may have had different experiences.
The story begins in the 19th Century, when computer was a job and this was someone who did computations, for scientists, labs, or for government. Even after first wave feminism, female education wasn’t universally available and the women in education tended to be women of privilege. After the end of the 19th century, women started to enter traditional universities to attempt to study PhDs (although often receiving a Bachelors for this work) but had few job opportunities on graduation, except teaching or being a computer. Whatever work was undertaken was inherently short-term as women were expected to leave the work force on marriage, to focus on motherhood.
During the early 20th Century, quantitative work was seen to be feminine and qualitative work required the rigour of a man – things have changed in perceptions, haven’t they! The women’s work was grunt work: calculating, microscopy. Then there’s men’s work: designing and analysing. The Wars of the 20th Century changed this by removing men and women stepping into the roles of men. Notably, women were stereotyped as being better coders in this role because of their computer background. Coding was clerical, performed by a woman under the direction of a male supervisor. This became male typed over time. As programming became more developed over the 50s and 60s and the perception of it as a dark art started to form a culture of asociality. Random hiring processes started to hurt female participation, because if you are hiring anyone then (quitting the speaker) if you could hire a man, why hire a woman? (Sound of grinding teeth from across the auditorium as we’re all being reminded of stupid thinking, presented very well for our examination by Elizabeth.)
CS itself stared being taught elsewhere but became its own school-discipline in the 60s and 70s, with enrolment and graduation of women matching that of physics very closely. The development of the PC and its adoption in the 80s changed CS enrolments in the 80s and CS1 became a weeder course to keep the ‘under qualified’ from going on to further studies in Computer Science. This then led to fewer non-traditional CS students, especially women, as simple changes like requiring mathematics immediately restricted people without full access to high quality education at school level.
In the 90s, we all went mad and developed hacker culture based around the gamer culture, which we already know has had a strongly negative impact on female participation – let’s face it, you don’t want to be considered part of a club that you don’t like and goes to effort to say it doesn’t welcome you. This led to some serious organisation of women’s groups in CS: Anita Borg Institute, CRA-W and the Grace Hopper Celebration.
Enrolments kept cycling. We say an enrolment boom and bust (including greater percentage of women) that matched the dot-com bubble. At the peak, female enrolment got as high as 30% and female faculty also increased. More women in academia corresponded to more investigation of the representation of women in Computer Science. It took quite a long time to get serious discussions and evidence identifying how systematic the under-representation is.
Over these different decades, women had very different experiences. The first generation had a perception that they had to give up family, be tough cookies and had a pretty horrible experience. The second generation of STEM, in 80s/90s, had female classmates and wanted to be in science AND to have families. However, first generation advisers were often very harsh on their second generation mentees as their experiences were so dissimilar. The second generation in CS doesn’t match neatly that of science and biology due to the cycles and the negative nerd perception is far, far stronger for CS than other disciplines.
Now to the third generation, starting in the 00s, outperforming their male peers in many cases and entering a University with female role models. They also share household duties with their partners, even when both are working and family are involved, which is a pretty radical change in the right direction.
If you’re running a mentoring program for incoming women, their experience may be very. very different from those of the staff that you have to mentor them. Finally, learning from history is essential. We are seeing more students coming in than, for a number of reasons, we may be able to teach. How will we handle increasing enrolments without putting on restrictions that disproportionately hurt our under-represented groups? We have to accept that most of our restrictions actually don’t apply in a uniform sense and that this cannot be allowed to continue. It’s wrong to get your restrictions in enrolment at a greater expense on one group when there’s no good reason to attack one group over another.
One of the things mentioned is that if you ask people to do something because of they are from group X, and make this clear, then they are less likely to get involved. Important note: don’t ask women to do something because they’re women, even if you have the intention to address under-representation.
The second paper, “Cultural Appropriation of Computational Thinking Acquisition Research: Seeding Fields of Diversity”, presented by Martha Serra, who is from Brazil and good luck to them in the World Cup tonight! Brazil adapted scalable game design to local educational needs, with the development of a web-ased system “PoliFacets”, seeding the reflection of IT and Educational researchers.
Brazil is the B in BRICS, with nearly 200 million people and the 5th largest country in the World. Bigger than Australia! (But we try harder.) Brazil is very regionally diverse: rain forest, wetlands, drought, poverty, Megacities, industry, agriculture and, unsurprisingly, it’s very hard to deal with such diversity. 80% of youth population failed to complete basic education. Only 26% of the adult population reach full functional literacy. (My jaw just dropped.)
Scalable Game Design (SGD) is a program from the University of Colorado in Boulder, to motivate all students in Computer Science through game design. The approach uses AgentSheets and AgentsCubes as visual programming environments. (The image shown was of a very visual programming language that seemed reminiscent of Scratch, not surprising as it is accepted that Scratch picked up some characteristics from AgentSheets.)
The SGD program started as an after-school program in 2010 with a public middle school, using a Geography teacher as the program leader. In the following year, with the same school, a 12-week program ran with a Biology teacher in charge. Some of the students who had done it before had, unfortunately, forgotten things by the next year. The next year, a workshop for teachers was introduced and the PoliFacets site. The next year introduced more schools, with the first school now considered autonomous, and the teacher workshops were continued. Overall, a very positive development of sustainable change.
Learners need stimulation but teachers need training if we’re going to introduce technology – very similar to what we learned in our experience with digital technologies.
The PolFacets systems is a live documentation web-based system used to assist with the process. Live demo not available as the Brazilian corner of internet seems to be full of football. It’s always interesting to look at a system that was developed in a different era – it makes you aware how much refactoring goes into the IDEs of modern systems to stop them looking like refugees from a previous decade. (Perhaps the less said about the “Mexican Frogger” game the better…)
The final talk (for both this session and the day) was “Apps for Social Justice: Motivating Computer Science Learning with Design and Real-World Problem Solving”, presented by Sarah Van Wart. Starting with motivation, tech has diversity issues, with differential access and exposure to CS across race and gender lines. Tech industry has similar problems with recruiting and retaining more diverse candidates but there are also some really large structural issues that shadow the whole issue.
Structurally, white families have 18-20 times the wealth of Latino and African-American people, while jail population is skewed the opposite way. The schools start with the composition of the community and are supposed to solve these distribution issues, but instead they continue to reflect the composition that they inherited. US schools are highly tracked and White and Asian students tend to track into Advanced Placement, where Black and Latino students track into different (and possibly remedial) programs.
Some people are categorically under-represented and this means that certain perspectives are being categorically excluded – this is to our detriment.
The first aspect of the theoretical prestige is Conceptions of Equity. Looking at Jaime Escalante, and his work with students to do better at the AP calculus exam. His idea of equity was access, access to a high-value test that could facilitate college access and thus more highly paid careers. The next aspect of this was Funds of Knowledge, Gonzalez et al, where focusing on a white context reduces aspects of other communities and diminishes one community’s privilege. The third part, Relational Equity (Jo Boaler), reduced streaming and tracking, focusing on group work, where each student was responsible for each student’s success. Finally,Rico Gutstein takes a socio-political approach with Social Justice Pedagogy to provide authentic learning frameworks and using statistics to show up the problems.
The next parts of the theoretical perspective was Computer Science Education, and Learning Sciences (socio-cultrual perspective on learning, who you are and what it means to be ‘smart’)
In terms of learning science, Nasir and Hand, 2006, discussed Practice-linked Identities, with access to the domain (students know what CS people do), integral roles (there are many ways to contribute to a CS project) and self-expression and feeling competent (students can bring themselves to their CS practice).
The authors produced a short course for a small group of students to develop a small application. The outcome was BAYP (Bay Area Youth Programme), an App Inventor application that queried a remote database to answer user queries on local after-school program services.
How do we understand this in terms of an equity intervention? Let’s go back to Nasir and Hand.
- Access to the domain: Design and data used together is part of what CS people do, bridging students’ concepts and providing an intuitive way of connecting design to the world. When we have data, we can get categories, then schemas and so on. (This matters to CS people, if you’re not one. 🙂 )
- Integral Roles: Students got to see the importance of design, sketching things out, planning, coding, and seeing a segue from non-technical approaches to technical ones. However, one other very important aspect is that the oft-derided “liberal arts” skills may actually be useful or may be a good basis to put coding upon, as long as you understand what programming is and how you can get access to it.
- Making a unique contribution: The students felt that what they were doing was valuable and let them see what they could do.
Take-aways? CS can appeal to so many peopleif we think about how to do it. There are many pathways to help people. We have to think about what we can be doing to help people. Designing for their own community is going to be empowering for people.
Sarah finished on some great questions. How will they handle scaling it up? Apprenticeship is really hard to scale up but we can think about it. Does this make students want to take CS? Will this lead to AP? Can it be inter-leaved with a project course? Could this be integrated into a humanities or social science context? Lots to think about but it’s obvious that there’s been a lot of good work that has gone into this.
What a great session! Really thought-provoking and, while it was a reminder for many of us how far we have left to go, there were probably people present who had heard things like this for the first time.
SIGSCE Day 2, “Focus on K-12: Informal Education, Curriculum and Robots”, Paper 1, 3:45-5:00, (#SIGCSE2014)Posted: March 8, 2014
The first paper is “They can’t find us: The Search for Informal CS Education” by Betsy DiSalvo, Cecili Reid, Parisa Khanipour Roshan, all from Georgia Tech. (Mark wrote this paper up recently.) There are lots of resources around, MOOCs, on-line systems tools, Khan academy and Code Academy and, of course the aggregators. If all of this is here, why aren’t we getting the equalisation effects we expect?
Well, the wealth and the resource-aware actually know how to search and access these, and are more aware of them, so the inequality persists. The Marketing strategies are also pointed at this group, rather than targeting those needing educational equity. The cultural values of the audiences vary. (People think Scratch is a toy, rather than a useful and pragmatic real-world tool.) There’s also access – access to technical resource, social support for doing this and knowledge of the search terms. We can address this issues by research mechanisms to address the ignored community.
Children’s access to informal learning is through their parents so how their parents search make a big difference. How do they search? The authors set up a booth to ask 16 parents in the group how they would do it. 3 were disqualified for literacy or disability reasons (which is another issue). Only one person found a site that was relevant to CS education. Building from that, what are the search terms that they are using for computer learning and why aren’t hey coming up with good results. The terms that parents use supported this but the authors also used Google insights to see what other people were using. The most popular terms for the topic, the environment and the audience. Note: if you search for kids in computer learning you get fewer results than if you search for children in computer learning. The three terms that came up as being best were:
- kids computer camp
- kids computer classes
- kids computer learning
The authors reviewed across some cities to see if there was variation by location for these search terse. What was the quality of these? 191 out of 840 search results were unique and relevant, with an average of 4.5 per search.
(As a note, MAN, does Betsy talk and present quickly. Completely comprehensible and great but really hard to transcribe!)
Results included : Camp, after school program, camp/afterschool, higher education, online activities, online classes/learning, directory results (often worse than Google), news, videos or social networks (again the quality was lower). Computer camps dominated what you could find on these search results – but these are not an option for low-income parents at $500/week so that’s not a really useful resource for them. Some came up for after school and higher ed in the large and midsize cities, but very little in the smaller cities. Unsurprisingly, smaller cities and lower socio-economic groups are not going to be able to find what they need to find, hence the inequality continues. There are many fine tools but NONE of them showed up on the 800+ results.
Without a background in CS or IT, you don’t know that these things exist and hence you can’t find it for your kids. Thus, these open educational resources are less accessible to these people, because they are only accessible through a mechanism that needs extra knowledge. (As a note, the authors only looked at the first two pages because “no-one looks past that”. 🙂 ) Other searches for things like kids maths learning, kids animal learning or kids physics learning turned up 48 out of 80 results (average of 16 unique results per search term), where 31 results were online, 101 had classes at uni – a big difference.
(These studies were carried out before code.org. Running the search again for kids computer learning does turn up code.org. Hooray, there is progress! If the study was run again, how much better would it be?)
We need to take a top down approach to provide standards for keywords and search terms, partnering with formal education and community programs. The MOOCs should talk to the Educational programming community, both could talk to the tutorial community and then we can throw in the Aggregators as well. Distant islands that don’t talk are just making this problem worse.
The bottom-up approach is getting an understanding of LSEO parenting, building communities and finding out how people search and making sure that we can handle it. Wow! Great talk but I think my head is going to explode!
During question time, someone asked why people aren’t more creative with their searches. This is, sadly, missing the point that, sitting in this community, we are empowered and skilled in searching. The whole point is that people outside of our community aren’t guaranteed to be able to find a way too be creative. I guess the first step is the same as for good teaching, putting ourselves in the heads of someone who is a true novice and helping to bring them to a more educated state.
It is an awful fact that women are very underrepresented in my discipline, Computer Science, and as an aggregate across my faculty, which includes Engineering and Mathematics (so we’re the Technology, Engineering and Mathematics of STEM). I have heard almost every tired and discredited excuse for why this is the case but what has always angered me is the sheer weight of resistance to any research that (a) clearly demonstrates that bias exists to explain why this occurs, (b) identifies how performance can be manipulated through preconceptions and (c) requires people to consider that we are all more similar than current representation would indicate.
Yes, if I were to look around and say “Women are not going to graduate in large numbers because I see so few of them” then I would be accurate and yet, at the same time, completely missing the point. If I were to turn that around and ask “Why are so few women coming in to my degree?” then I have a useful question and, from various branches of research, the more rocks we turn over, the more we seem to find bias (conscious or otherwise) in both industry and academia that discourages women from participation in STEM.
A paper was recently published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS, to its friends), entitled “Science faculty’s subtle gender biases favor male students”. (PNAS has an open access option but the key graphs and content are also covered in a Scientific American blog article.) The study was simple. Take a job application for a lab manager position. Assign a name where half of the names are a recognisably male name, the other half are female. (The names John and Jennifer were chosen for this purpose as they had been pre-tested to be equivalent in terms of likability and recogniseability.) Get people to rate the application, including aspects like degree of mentoring offered and salary.
Let me summarise that: the name John or Jennifer is assigned to the same application materials. What we would expect, if there is no bias, is that we would see a similar ranking and equivalent salary offering. (All figures from the original paper, via the SciAm link.)
Oh. It appears that the mere presence of a woman’s name somehow altered reality so that an objective assessment of ability was warped through some sort of … I give up. Humour has escaped me. The name change has resulted in a systematic and significant downgrading of perceived ability. Let me get the next graph out of the way which is the salary offer.
And, equally mysteriously, having the name John is worth over $3,500 more than having the name Jennifer.
I should leap to note that it was both male and female scientists making this classification – which starts to lead us away from outright misogyny and towards ingrained and subtler prejudices. Did people resort to explicitly sexist reasoning to downgrade the candidates? No, they used sound reasoning to argue against the applicant’s competency. Except, of course, we draw back the curtain and suddenly reveal that our sound reasoning works one way when the applicant is a man, another if they are a woman.
Before you think “Oh, they must have targeted a given field, age group or gone after people who do or don’t have tenure”, the field, age and tenure status of the rating professors had no significant effect. This bias is pervasive among faculty, field, age, gender and status. The report also looked at mentoring and, regardless of the rater’s gender, they offered less mentoring to women.
Let’s be blunt. Study after study shows that if there are any gender differences at all, they are so small as to not even vaguely explain what we see in the representation of female students in certain fields and completely fails to explain their reduced progress in later life. However, the bias and stereotypes that people are operating under do not so much predict what will happen as shape what will happen. We are now aware of effects such as Stereotype Threat (Wiki link) that allows us to structure important situations in someone’s life so that the framing of the activity leaves them in a position where they reinforce the negative stereotype because of higher anxiety, relative to a non-stereotyped group. As an example, look at Osborne, Linking Stereotype Threat and Anxiety, where you can actually reduce the performance of girls on a maths test through reminding them that they are girls and that girls tend to do worse on test than boys. Osborne then compared this with a group where the difference was identified but a far more positive statement was made (the participants were told that despite the difference, there were situations where girls performed as well or better). The first scenario (girls do worse) was a high Stereotype Threat scenario (high ST), the second is low ST. Here’s the graph from Wikipedia that is a redrawing of the one in the paper that shows the results.
That is the impact of an explicit stereotype in action – suddenly, when framed fairly and without an explicit stereotype or implicit bias, we see that people are far more similar than we thought. If anything, we have partially inverted the stereotype.
To return to my first paragraph, I said:
what has always angered me is the sheer weight of resistance to any research that (a) clearly demonstrates that bias exists to explain why this occurs, (b) identifies how performance can be manipulated through preconceptions and (c) requires people to consider that we are all more similar than current representation would indicate.
The PNAS paper, among others, clearly shows that the biasses exist. A simple name change is enough, as long as it’s a woman’s name. The demonstrated existence of stereotype threat shows us how performance can be manipulated through preconception. (And it’s important to note that stereotype threat is as powerful against minorities as woman – anyone who is part of a stereotype can be manipulated through their own increased or reduced anxiety.) So let me finally discuss the consideration of all of this and the title of this post.
I am expecting to get at least one person howling me down. Someone who will tear apart all of this because this cannot, possibly, under any circumstances be true. Someone who will start talking about our “African ancestors” to start arguing the Savanna-distribution of roles, as if our hominid predecessors ever had to apply to be a lab manager anywhere. Most of you, I hope, will read this and know all of this far too well. Some of you will reflect on this and, like me, examine yourself very carefully to find out if you have been using this bias or if you have been framing things, while trying to help, in a way that really didn’t help at all.
Some of you, who are my students, will read this and will see that research that you have done is reflected in these figures. Yes, we treat women differently and we appear, in these circumstances, to treat them less well. This does not, under any circumstances, mean that we have to accept this or, in any way, respect this as an established tradition or a desirable status quo. But the detection of an insidious and pervasive bias, that spans a community, shows us how hard my point (c) actually is.
We must first accept that there is a problem. There is a problem. Denying it will achieve nothing. Arguing minutiae will achieve nothing. We have to change the way that we react and be honest with ourselves that, sometimes, our treasured objectivity is actually nothing of the kind.
Oh, the poor students that I spoke to today. We have a new degree program starting, the Bachelor of Computer Science (Advanced), and it’s been given to me to coordinate and set up the first course: Grand Challenges in Computer Science, a first-year offering. This program (and all of its unique components) are aimed at students who have already demonstrated that they have got their academics sorted – a current GPA of 6 or higher (out of 7, that’s A equivalent or Distinctions for those who speak Australian), or an ATAR (Australian Tertiary Admission Rank) of 95+ out of 100. We identified some students who met the criteria and might want to be in the degree, and also sent out a general advertisement as some people were close and might make the criteria with a nudge.
These students know how to do their work and pass their courses. Because of this, we can assume some things and then build to a more advanced level.
Now, Nick, you might be saying, we all know that you’re (not so secretly) all about equality and accessibility. Why are you running this course that seems so… stratified?
Ah, well. Remember when I said you should probably feel sorry for them? I talked to these students about the current NSF Grand Challenges in CS, as I’ve already discussed, and pointed out that, given that the students in question had already displayed a degree of academic mastery, they could go further. In fact, they should be looking to go further. I told them that the course would be hard and that I would expect them to go further, challenge themselves and, as a reward, they’d do amazing things that they could add to their portfolios and their experience bucket.
I showed them that Cholera map and told them how smart data use saved lives. I showed them We Feel Fine and, after a slightly dud demo where everyone I clicked on had drug issues, I got them thinking about the sheer volume of data that is out there, waiting to be analysed, waiting to tell us important stories that will change the world. I pretty much asked them what they wanted to be, given that they’d already shown us what they were capable of. Did they want to go further?
There are so many things that we need, so many problems to solve, so much work to do. If I can get some good students interested in these problems early and provide a coursework system to help them to develop their solutions, then I can help them to make a difference. Do they have to? No, course entry is optional. But it’s so tempting. Small classes with a project-based assessment focus based on data visualisation: analysis, summarisation and visualisation in both static and dynamic areas. Introduction to relevant philosophy, cognitive fallacies, useful front-line analytics, and display languages like R and Processing (and maybe Julia). A chance to present to their colleagues, work with research groups, do student outreach – a chance to be creative and productive.
I, of course, will take as much of the course as I can, having worked on it with these students, and feed parts of it into outreach into schools, send other parts in different levels of our other degrees. Next year, I’ll write a brand new grand challenges course and do it all again. So this course is part of forming a new community core, a group of creative and accomplished leaders, to an extent, but it is also about making this infectious knowledge, a striving point for someone who now knows that a good mark will get them into a fascinating program. But I want all of it to be useful elsewhere, because if it’s good here, then (with enough scaffolding) it will be good elsewhere. Yes, I may have to slow it down elsewhere but that means that the work done here can help many courses in many ways.
I hope to get a good core of students and I’m really looking forward to seeing what they do. Are they up for the challenge? I guess we’ll find out at the end of second semester.
But, so you know, I think that they might be. Am I up for it?
I certainly hope so! 🙂